Data Migration for Distribution ERP: What to Bring, What to Archive, What to Leave Behind
The ERP implementation was progressing well until the data migration team opened the customer master file. What should have been 8,000 active customers turned out to be 23,000 records spanning 30 years—including customers who’d gone out of business a decade ago, duplicate entries for the same companies with slight name variations, addresses that were outdated or incomplete, and pricing agreements that expired years ago but were never cleaned up.
The product master was even worse: 45,000 SKUs when the company actively sold only 12,000. The rest were discontinued items, duplicate entries with different numbering schemes from system migrations past, supplier part numbers mixed with internal numbers, and items nobody could identify or explain.
Faced with this chaos, the implementation team made a fateful decision: “Let’s just migrate everything. We’ll clean it up later in the new system.” This choice, made to save a few weeks of cleanup work, would haunt operations for years through slow system performance from bloated databases, confusion from duplicate and obsolete data, wasted time searching through irrelevant information, reports cluttered with meaningless data, and cleanup work that never happened because “later” never came.
Data migration decisions rank among the most critical and underestimated aspects of ERP implementation. Migrate too much and you carry decades of garbage into your new system. Migrate too little and you lose critical business information. Get the balance right and you start your new ERP journey with clean, accurate data that enables rather than hinders operations.
The Data Migration Dilemma
The “Migrate Everything” Trap
When facing data migration decisions, the path of least resistance is migrating everything from old systems. This approach feels safe—you won’t lose anything important—and seems faster than careful analysis and cleanup.
But migrating everything creates lasting problems through degraded system performance from excessive data volume, confusion from duplicate and obsolete records, cluttered search results and reports, maintenance burden for irrelevant data, storage costs for unnecessary information, and missed opportunity to start fresh.
Data is not like physical belongings where keeping extra items just fills closets. In ERP systems, every unnecessary record slows searches, clutters drop-downs, appears in reports, and creates confusion about what’s current and relevant.
The “Start Fresh” Fantasy
The opposite extreme—migrating minimal data to start completely fresh—sounds appealing but creates different problems through lost historical context for business decisions, inability to analyze trends over time, missing customer purchase history, lack of vendor performance data, absence of product movement patterns, and loss of institutional knowledge.
Distribution businesses need historical data to forecast demand, analyze profitability, understand customer patterns, and make informed decisions. Starting with blank slates eliminates valuable business intelligence.
Finding the Right Balance
Effective data migration requires strategic decisions about what data is essential for ongoing operations, what historical information provides business value, what can be archived for reference but not migrated, what is truly obsolete and can be discarded, and how far back transaction history should extend.
These decisions should be made deliberately based on business requirements, not defaulted to “everything” because decision-making seems hard.
Master Data Migration Decisions
Customer Master Data
What to Bring:
- Active customers who purchased within the past 2-3 years
- Current billing and shipping addresses
- Active pricing agreements and terms
- Credit limits and payment terms
- Current contact information
- Active contract terms and conditions
What to Archive:
- Customers inactive for 3+ years but potentially reactivatable
- Historical addresses for reference
- Expired pricing agreements for reference
- Historical purchase patterns and analysis
- Old contact information
- Correspondence and notes
What to Leave Behind:
- Customers inactive for 5+ years with no prospect of return
- Duplicate customer records
- Test accounts from old system
- Customers who explicitly closed accounts
- Obviously erroneous records
- Incomplete records with minimal information
The Cleanup Opportunity: Use migration as an opportunity to consolidate duplicate customer records, standardize naming conventions, validate and update addresses, verify and complete contact information, review and update credit terms, and eliminate clearly obsolete data.
Product Master Data
What to Bring:
- Currently active SKUs in inventory or on order
- Products sold within the past 2 years
- Current pricing and cost information
- Active product specifications and attributes
- Current supplier information
- Product relationships (substitutes, alternatives)
What to Archive:
- Discontinued products sold within past 3-5 years
- Historical pricing for analysis
- Product performance data
- Supersession history
- Historical supplier relationships
- Old specifications for reference
What to Leave Behind:
- Products discontinued 5+ years ago with no transaction history
- Duplicate product records
- Test SKUs and system setup artifacts
- Products with zero transaction history
- Incomplete or erroneous records
- Placeholder items never actually used
The Cleanup Opportunity: Consolidate duplicate SKUs, standardize product numbering, complete missing product attributes, update product descriptions, verify unit of measure consistency, and eliminate zombie products nobody can identify.
Vendor Master Data
What to Bring:
- Active vendors purchased from in past 2 years
- Current remit-to addresses
- Active payment terms
- Current contacts and rep information
- Lead times and MOQ information
- Current contractual relationships
What to Archive:
- Vendors inactive for 2-5 years
- Historical pricing and terms
- Purchase history for analysis
- Performance metrics and history
- Historical contact information
- Correspondence history
What to Leave Behind:
- Vendors inactive 5+ years with no relationship
- Duplicate vendor records
- Vendors who went out of business
- One-time vendors for specific projects completed long ago
- Test vendor accounts
- Clearly erroneous entries
The Cleanup Opportunity: Consolidate vendor duplicates, standardize vendor names, validate remit-to addresses, update contact information, verify tax ID accuracy, and remove obvious duplicates or errors.
Transactional Data Migration Decisions
Open Transactions
What to Bring:
- All open sales orders awaiting fulfillment
- Open purchase orders awaiting receipt
- Open accounts receivable invoices
- Open accounts payable invoices
- Active backorders
- In-transit inventory
- Pending credits and adjustments
- Current period transactions
What NOT to Bring: Open transactions must migrate completely. Attempting to start your new system without open orders, receivables, or payables creates operational chaos and financial inaccuracy.
The Critical Success Factor: Open transactions are the highest risk migration area. Every open order must migrate accurately with correct customer, product, pricing, and status. Every receivable must match to the dollar. Every payable must be accurate and timely for payment processing.
Historical Transaction Data
What to Bring:
- Order history for past 2-3 years minimum
- Sales history by customer and product
- Purchase history for supplier analysis
- Payment history for customer credit assessment
- Prior period financial statements
- Inventory transaction history (1-2 years)
What to Archive:
- Transactions from 3-7 years ago for long-term analysis
- Detailed transaction records beyond what’s needed operationally
- Historical reporting and analytics
- Audit trail information
- Regulatory required retention
- Closed financial periods
What to Leave Behind:
- Transaction detail beyond 7-10 years (unless regulatory requirements)
- Incomplete or corrupted historical transactions
- Test transactions from old system
- Transactions that can’t be validated or reconciled
- System-generated transactions with no business meaning
The Balance Point: More transaction history enables better forecasting and analysis. But migrating 10+ years of detailed transactions may not be worth the effort and performance impact. Focus on the sweet spot of 2-5 years of detailed history with summary-level data for longer periods.
Inventory Data
What to Bring:
- Current on-hand quantities by location
- Current inventory valuation and cost
- Lot numbers and serial numbers for traceable inventory
- Inventory reservations and allocations
- Location and bin assignments
- Inventory ownership (owned vs. consignment)
- Quality holds and restrictions
What to Archive:
- Historical inventory levels and valuations
- Inventory movement patterns
- Cycle count history and accuracy trends
- Adjustment history
- Obsolete inventory write-off history
What to Leave Behind:
- Movement history beyond what’s needed for analysis
- Inventory for products no longer carried
- Historical lot numbers for consumed inventory
- Archived location structures no longer used
The Critical Success Factor: Inventory migration must be precisely accurate. Physical inventory or careful reconciliation immediately before migration is essential. Starting your new system with incorrect inventory quantities or valuations creates problems that can take months to resolve.
Data Quality and Cleanup
The Pre-Migration Cleanup
Don’t migrate dirty data hoping to clean it later. Clean before migration including identifying and merging duplicates, correcting obvious errors, completing missing information, standardizing formats and conventions, validating key data fields, and eliminating obviously obsolete records.
Every dollar spent on pre-migration cleanup saves five dollars in post-migration correction and ongoing confusion.
The 80/20 Rule
Perfect data is impossible and unnecessary. Focus cleanup efforts on the vital few through customer data for top 20 percent of revenue, product data for active SKUs, vendor data for primary suppliers, transactional data accuracy, and critical fields required by new system.
Don’t let perfection become the enemy of good enough. Focus on what matters most for business operations.
Data Validation Strategies
Implement systematic validation including cross-field validation (zip code matches city/state), range checks (quantities and prices within reasonable bounds), referential integrity (every transaction references valid master records), format standardization (phone numbers, addresses), and business rule compliance.
Automated validation catches most errors efficiently. Manual review handles exceptions and edge cases.
The Validation Cycle
Plan multiple validation rounds including initial assessment identifying issues, cleanup iteration correcting problems, validation testing confirming fixes, final validation before migration, and post-migration reconciliation confirming accuracy.
Don’t expect one pass to catch everything. Budget time for multiple cleanup and validation cycles.
Technical Migration Considerations
Migration Approach Options
Choose the right approach for your data including automated migration tools for high-volume standard data, custom ETL (Extract, Transform, Load) for complex transformations, manual entry for small datasets or complex records, and hybrid approaches combining methods.
The right approach depends on data volume, complexity, quality, and available resources.
Staging and Testing
Never migrate directly to production. Use staging approach including development environment for initial testing, test environment for UAT, multiple test migration iterations, data validation after each iteration, and production migration only after successful testing.
Each test migration reveals issues requiring correction before attempting production migration.
Cutover Planning
Plan the actual migration cutover including system freeze timing, migration execution window, validation checkpoints, rollback procedures if needed, and go/no-go decision criteria.
Production migrations typically happen during off-hours or weekends to minimize business disruption. Plan for sufficient time without rushing.
Reconciliation Requirements
Post-migration reconciliation proves accuracy through customer count and key attributes, product count and inventory value, vendor count and open payables, open order counts and values, accounts receivable balance, inventory quantities and values, and financial statement balances.
Every critical balance must reconcile perfectly before declaring migration successful.
Common Migration Mistakes
Underestimating Effort
Data migration consistently takes longer than expected. Common underestimates include data quality assessment time, cleanup and correction effort, transformation development complexity, testing and validation cycles, issue resolution time, and reconciliation work.
Budget 25-50 percent more time than initial estimates suggest.
Neglecting Data Ownership
Every data element needs an owner responsible for accuracy and completeness. Without clear ownership, nobody validates correctness, cleanup doesn’t happen, errors persist, and blame-shifting replaces accountability.
Assign specific people responsibility for customer data, product data, vendor data, transactional accuracy, and overall migration success.
Postponing Cleanup
“We’ll fix it after migration” never works. Issues become harder to fix in new system, momentum to clean up disappears, operational urgency supersedes cleanup, and dirty data becomes entrenched.
Clean data before migration or accept that it won’t get cleaned.
Over-Customizing Migration
Every custom transformation and special handling adds complexity, time, cost, and maintenance burden. Avoid unnecessary customization by adapting to new system’s data structure when reasonable, accepting some manual cleanup instead of automation for edge cases, focusing custom work on highest-value data, and standardizing rather than perpetuating old system’s quirks.
Ignoring User Acceptance
Technical migration success doesn’t guarantee user acceptance. Involve users in validation testing with migrated data, confirm data makes sense operationally, verify reports show expected information, and get explicit sign-off before go-live.
Users find data problems that technical validation misses.
The Bizowie Migration Advantage
Bizowie’s proven data migration methodology systematically addresses every critical decision through structured assessment of current data, clear decision frameworks for what to migrate, data quality analysis and cleanup tools, migration templates for common entities, validation and reconciliation procedures, and experienced migration team support.
Our cloud platform simplifies migration through flexible data import capabilities, automated validation rules, intuitive data mapping, test environment for validation, and rollback capabilities if issues arise.
Distribution companies implementing Bizowie benefit from our migration experience across hundreds of implementations, understanding of distribution data patterns, and commitment to clean, accurate starting data.
Creating Your Migration Strategy
Assessment Phase
Begin with thorough assessment including current data inventory and volumes, data quality analysis, historical data value assessment, regulatory retention requirements, and migration complexity evaluation.
Understanding what you have is prerequisite for deciding what to migrate.
Decision Framework
Make systematic decisions about each data category using business value consideration, operational necessity, historical analysis requirements, regulatory compliance needs, and cost-benefit of migration versus archival.
Document decisions and rationale for stakeholder review and approval.
Cleanup Planning
Develop comprehensive cleanup plan including issue prioritization, resource allocation, cleanup procedures and tools, validation approach, and timeline and milestones.
Cleanup is project-within-project requiring dedicated management.
Execution and Validation
Execute migration systematically through multiple test iterations, comprehensive validation, issue resolution, final production migration, and thorough reconciliation.
Don’t rush. Accuracy matters more than speed.
Conclusion
Data migration decisions have lasting impact on your new ERP system’s performance and usability. Migrate too much garbage and you carry decades of confusion forward. Migrate too little and you lose valuable business information and history.
The right approach requires strategic thinking about what data truly matters for ongoing operations versus what can be archived for reference or discarded completely. It demands disciplined cleanup before migration rather than hoping to fix problems later. And it necessitates thorough validation and reconciliation to ensure accuracy.
Distribution companies that treat data migration as strategic activity rather than technical chore achieve better implementations with cleaner data, better performance, higher user satisfaction, and stronger foundation for future growth.
Modern cloud ERP platforms like Bizowie provide tools, templates, and methodologies that make effective data migration achievable. But tools alone aren’t enough—thoughtful decisions about what to bring, archive, and leave behind separate successful migrations from problematic ones.
Your new ERP deserves clean, accurate, relevant data. Invest the time and discipline to make the right migration decisions. The operational benefits of starting with quality data compound daily for years after implementation.
Don’t let data migration be an afterthought or technical exercise. Make it the strategic activity it deserves to be—the foundation on which everything else in your new system depends.