Data Migration Best Practices When Switching to New ERP

Data migration represents one of the highest-risk aspects of ERP implementation. Get it right, and your new system launches with accurate, accessible information. Get it wrong, and you face months of correcting errors, lost historical data, and frustrated users questioning the entire investment.

Successful data migration requires methodical planning, rigorous execution, and realistic timelines. This guide provides the framework precast manufacturers need to transition data from legacy systems to new ERP platforms without operational disruption.

Understanding the Data Migration Challenge

Data migration complexity varies dramatically based on your starting point and what you're moving to the new system.

Common Source Scenarios

• Multiple spreadsheets: Simpler migration but limited historical data structure
• Legacy ERP system: Comprehensive data but complex extraction and mapping
• Industry-specific software: Good data organization but may lack standard export formats
• Hybrid systems: Data scattered across multiple disconnected applications

What Data to Migrate

Not all data deserves migration. Prioritize based on business value:

• Master data (essential): Customers, vendors, employees, products, charts of accounts
• Open transactions (essential): Active projects, outstanding invoices, unfilled orders
• Historical transactions (selective): Past orders, completed projects, financial history
• Reference data (assess value): Old product catalogs, inactive customers, archived documents

Phase 1: Planning and Assessment

Data Inventory

Document all data sources and what they contain:

1. Identify every system, database, and spreadsheet holding business-critical information
2. Catalog data types, volumes, and current quality levels
3. Assess data relationships and dependencies
4. Identify data owners responsible for accuracy verification

Data Quality Assessment

Evaluate current data quality across critical dimensions:

• Accuracy: Is the data correct and free from errors?
• Completeness: Are all required fields populated?
• Consistency: Is data formatted uniformly across records?
• Currency: Is the data up-to-date and relevant?
• Uniqueness: Are there duplicate records to consolidate?

Migration Strategy Selection

Choose the appropriate approach:

• Big bang migration: Move all data in single cutover event (higher risk, faster completion)
• Phased migration: Move data in stages by module or timeframe (lower risk, extended timeline)
• Parallel operation: Run both systems temporarily while validating data (safest but resource-intensive)

Phase 2: Data Cleansing and Preparation

Migration presents the perfect opportunity to clean up years of accumulated data problems. Never migrate dirty data.

Common Data Quality Issues

Duplicate Records

• Multiple customer records for the same company with slight name variations
• Duplicate products with different codes
• Redundant vendor entries created over time

Incomplete Information

• Missing addresses, contact information, or tax IDs
• Partial product specifications
• Incomplete cost data or pricing information

Inconsistent Formatting

• Phone numbers in multiple formats (dashes, dots, spaces)
• Addresses with non-standard abbreviations
• Dates in various formats
• Inconsistent capitalization and spelling

Obsolete Data

• Inactive customers from years ago
• Discontinued products
• Former employees still in the system
• Outdated pricing or cost information

Cleansing Approach

1. Automated cleanup: Use tools to standardize formats, remove obvious duplicates
2. Manual review: Have data owners verify and correct critical records
3. Validation rules: Apply business rules to identify questionable data
4. Archival decisions: Determine what to migrate vs. archive vs. delete

Phase 3: Data Mapping

Data mapping defines how information from source systems translates to the new ERP structure.

Creating Mapping Documents

For each data type, document:

• Source field: Where the data currently resides
• Target field: Corresponding field in new ERP
• Transformation rules: Any conversion or calculation required
• Default values: What to use if source data is blank
• Validation criteria: Rules to verify data accuracy

Common Mapping Challenges

One-to-Many Relationships

A single legacy field may need to populate multiple fields in the new system. Example: A combined "Name" field splitting into "First Name" and "Last Name."

Many-to-One Consolidation

Multiple source fields might consolidate into a single target field. Example: Separate labor cost fields combining into one total labor cost field.

Value Translation

Codes or categories that differ between systems require translation tables. Example: Old system uses A/B/C priority codes while new system uses High/Medium/Low.

Phase 4: Testing and Validation

Never migrate data directly into the production environment. Thorough testing is essential.

Test Migration Cycles

Cycle 1: Initial Load

• Load master data into test environment
• Verify record counts match expectations
• Identify obvious errors or mapping issues
• Refine cleansing and mapping procedures

Cycle 2: Refinement

• Reload data with corrections applied
• Add transactional data to master records
• Test data relationships and dependencies
• Validate business rules and calculations

Cycle 3: Final Rehearsal

• Execute complete migration as it will occur at go-live
• Time the migration process to plan cutover window
• Verify all validation checks pass
• Have end users test with migrated data

Validation Procedures

• Record count reconciliation: Verify expected number of records migrated
• Hash total verification: Check financial totals match between systems
• Sampling review: Manually verify representative sample of records
• Relationship testing: Confirm linked data connects properly (e.g., orders to customers)
• Business process testing: Execute end-to-end workflows with migrated data

Phase 5: Production Migration

Pre-Migration Checklist

• Final backup of all source systems
• All data cleansing and preparation completed
• Mapping documents finalized and approved
• Test migrations completed successfully
• Validation procedures documented and ready
• Rollback plan prepared if migration fails
• Migration team and schedule confirmed

Migration Execution

1. Cutoff transactions: Freeze legacy system at defined point
2. Extract data: Pull data from source systems using approved scripts
3. Transform data: Apply cleansing and mapping rules
4. Load data: Import into production ERP environment
5. Validate results: Execute all validation procedures
6. User verification: Have subject matter experts review their data
7. Go/no-go decision: Formally approve migration or execute rollback

Post-Migration Monitoring

The first days after migration are critical:

• Monitor system performance with production data volumes
• Track and rapidly resolve user-reported data issues
• Document any additional data corrections needed
• Maintain legacy system access for reference during transition period

Best Practices for Success

Start Early

Begin data assessment and cleansing months before implementation. Data quality improvement takes longer than anticipated, and early starts reduce pressure during critical go-live periods.

Assign Data Ownership

Each data type needs an owner responsible for accuracy verification. Project managers can coordinate, but operational experts must validate their data.

Document Everything

Comprehensive documentation enables:

• Consistency across multiple migration cycles
• Clear communication with all stakeholders
• Troubleshooting when issues arise
• Knowledge transfer to support teams

Plan for Iterative Testing

Budget time for 3-4 complete test migration cycles. Each iteration reveals issues that require correction before the next cycle.

Set Realistic Expectations

Even with perfect migration execution, expect some data issues to surface after go-live. Plan for ongoing data quality monitoring and correction processes.

Common Migration Mistakes to Avoid

• Underestimating complexity: Migration always takes longer than initial estimates
• Migrating dirty data: Garbage in, garbage out applies to ERP systems
• Insufficient testing: Discovering errors after go-live is exponentially more expensive
• Lack of data ownership: IT cannot validate business data accuracy alone
• No rollback plan: Always have a contingency if migration fails
• Inadequate documentation: Undocumented decisions create problems months later
• Ignoring data relationships: Orphaned records break business processes

Conclusion

Data migration success requires methodical planning, disciplined execution, and adequate time allocation. Companies that invest properly in data migration lay the foundation for ERP systems that deliver immediate value and long-term operational excellence.

The manufacturers who struggle with ERP implementations typically rushed data migration, accepted poor data quality, or skipped thorough testing. Don't let timeline pressure compromise this critical phase—the operational consequences extend far beyond implementation completion.

Remember that migration is not just a technical exercise. It's an opportunity to establish clean, accurate data that drives better decision-making, streamlined operations, and sustainable competitive advantage.