What is data migration in ERP?

Data migration is the process of moving data from legacy systems to the new ERP. It includes extracting data, cleansing it, transforming to fit the new structure, and loading it – often with multiple test cycles.

What are the common challenges in data migration?

Poor data quality (duplicates, missing values), legacy system complexity, volume, tight timelines, and lack of business ownership are typical challenges.

ETL stands for Extract, Transform, Load – the core process of data migration. Data is extracted from source systems, transformed (cleansed, mapped, formatted), and loaded into the new ERP.

Data migration strategy

From ERPEDIA, the independent ERP knowledge base

Data migration is the process of moving data from legacy systems to the new ERP. It involves extracting, cleansing, transforming, and loading data – often across multiple test cycles. Poor data migration is a leading cause of ERP project failure. A well‑planned strategy ensures data integrity, minimises downtime, and sets the foundation for successful system adoption.

Contents

1. Why data migration matters
2. ETL process overview
3. Migration phases
4. Data cleansing & quality
5. Validation & reconciliation
6. Migration tools
7. Common pitfalls & risks
8. Cutover & go‑live

1. Why data migration matters

Data is the lifeblood of an ERP system. Common consequences of poor migration:

Incorrect financial reporting
Duplicate or missing customer/vendor records
Inventory inaccuracies leading to stockouts
Loss of trust in the new system

Fact: 50% of ERP implementations experience delays due to data migration issues. 30% of project budgets are often spent on data cleansing.

2. ETL process overview

Extract → Transform → Load

Extract: Retrieve data from legacy systems (databases, spreadsheets, legacy ERP).
Transform: Cleanse, map, and format data to fit the new ERP structure.
Load: Import data into the new ERP tables.

3. Migration phases

A structured approach typically includes:

Phase	Activities
1. Discovery & assessment	Inventory data sources, assess quality, define scope (master vs transactional data).
2. Data cleansing & preparation	Standardise, deduplicate, enrich, and archive old data.
3. Migration design & mapping	Define source‑to‑target mappings, transformation rules.
4. Test migrations	Multiple cycles (dry runs) to validate process and data quality.
5. Final migration (cutover)	Migrate production data during go‑live window.
6. Post‑migration validation	Reconcile totals, verify transactions, user acceptance.

4. Data cleansing & quality

Common data quality issues and fixes:

Duplicates Merge or delete duplicate customer/vendor records.
Missing values Fill mandatory fields (e.g., tax IDs, payment terms).
Inconsistent formats Standardise dates, currencies, units of measure.
Invalid references Ensure all foreign keys (e.g., customer IDs on orders) exist.

Data cleansing is often the most time‑consuming part of migration – start early!

Tip: Use data profiling tools to analyse source data quality before starting.

5. Validation & reconciliation

After each test migration, validate:

Record counts: Do source and target match for each entity?
Financial totals: Sum of open AR, inventory value, general ledger balances.
Sampling: Business users verify a subset of records (e.g., top 10 customers).
Referential integrity: Orders link to valid customers, items, etc.

Reconciliation reports are signed off by process owners.

6. Migration tools

Tool type	Examples
ERP‑specific data import	Data import wizards, templates (Excel, CSV) – e.g., SAP LSMW, Odoo import
ETL tools	Microsoft SSIS, Talend, Pentaho, Informatica
Data quality tools	Trillium, Ataccama, Experian
Custom scripts	Python, SQL for complex transformations

7. Common pitfalls & risks

Underestimating effort: Data cleansing takes 2–3x longer than expected.
No business ownership: IT cannot fix data alone – business must lead cleansing.
Migrating everything: Archive old, irrelevant data – don't migrate garbage.
Skipping test cycles: First time should never be at go‑live.
No rollback plan: What if final migration fails?

8. Cutover & go‑live

The final migration occurs during the cutover window. Key steps:

Freeze data entry in legacy systems.
Extract final delta (changes since last test).
Run transformation and load into ERP.
Perform quick validation (counts, totals).
Open ERP for business.

A rollback plan should be ready if issues are critical.

Key Takeaways

Data migration is a project within the project – plan early and allocate sufficient time.
Cleanse data before migration; don't move garbage.
Multiple test cycles (dry runs) are essential to validate process and data.
Business ownership of data quality is critical.
Reconcile record counts and financial totals after each migration.

Should I migrate all historical data? Not necessarily. Consider archiving old transactions; migrate only what's needed for operations and legal compliance.

What is a data migration strategy document? A formal plan covering scope, approach, timelines, tools, responsibilities, and validation criteria.

How many test migrations are enough? Typically 2–3 full dry runs, plus incremental tests for specific data sets.

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