Data Migration Strategy & Execution

Part 4 of 11 in the Business Central Implementation Series

Published: December 2025 | Reading Time: 14 minutes

Introduction

Data migration is often the most underestimated yet critical phase of Business Central implementation. It's the bridge between your legacy systems and your new ERP platform—the process that brings your business history, relationships, and operational data into your configured Business Central environment.

While it might be tempting to view data migration as a simple "lift and shift" operation, successful migrations require careful planning, rigorous data cleansing, systematic validation, and meticulous execution. Poor data migration can cripple an otherwise excellent implementation, while a well-executed migration sets the stage for confident go-live and long-term success.

This comprehensive guide provides a strategic framework for planning and executing Business Central data migration, from initial assessment through final cutover validation.

📋 Business Central Data Migration Steps (8-Phase Process)

1. Assessment Phase: Analyze source data quality, volume, and structure; identify data cleansing needs

2. Planning Phase: Define migration scope (master data, open transactions, historical data), select tools, and establish cutover timeline

3. Design Phase: Create detailed data mapping (source → target fields), transformation rules, and validation criteria

4. Tool Selection: Choose migration method: Configuration Packages (Excel), API-based tools (Azure Data Factory, Power Automate), or custom AL extensions

5. Data Cleansing: Remove duplicates, standardize formats, validate master data, and fix legacy data issues before migration

6. Testing Phase: Execute test migrations in Sandbox environment, validate data accuracy, and reconcile to source system

7. Cutover Execution: Freeze legacy system, perform final data extraction, load into Production Business Central, and validate critical balances

8. Post-Migration Validation: Reconcile opening balances, verify customer/vendor aging, test transactions, and confirm reporting accuracy

Typical Timeline: 3-6 weeks for data migration phase depending on data volume and complexity.

💡 Pricing & Timeline Note
All cost estimates and timelines in this article reflect typical Business Central implementations as of January 2026.

• Geographic Context: Estimates based on Western Europe and North America markets

• Regional Variation: Implementation costs vary significantly by region (typically 30-60% lower in Eastern Europe, Asia-Pacific, and Latin America)

• Microsoft Licensing: Verify current prices at aka.ms/BCPricing as these change periodically

• Effort-Based Budgeting: Use the consulting hours estimates with your local partner's rates for accurate budgeting

These are reference estimates for planning purposes. Request detailed quotes from Microsoft Solutions Partners for your specific requirements.

Data Migration Planning and Strategy

Effective migration begins with comprehensive planning that balances completeness with practicality.

Defining Migration Scope

Not all data deserves migration—strategic decisions about what to migrate and what to archive are essential.

Master Data vs. Transactional Data:

Master Data (Always migrate):

• Customers and customer contacts

• Vendors and vendor contacts

• Items and inventory records

• Chart of accounts

• Fixed assets

• Employees (if using Business Central for HR/payroll)

• Price lists and discounts

• Bill of materials (for manufacturing)

Transactional Data (Selective migration):

• Open Transactions (Must migrate):

  • Open customer invoices and credit memos

  • Open vendor invoices and credit memos

  • Open purchase orders

  • Open sales orders

  • Open bank transactions

• Historical Transactions (Evaluate carefully):

  • Posted invoices and receipts

  • Payment history

  • General ledger history

  • Inventory transactions

  • Closed orders and quotes

Historical Data Decisions:

Full History Migration:

• Pros: Complete business history available, comprehensive reporting, audit trail continuity

• Cons: Extended migration time, larger database, more validation required, higher cost

• Best for: Regulated industries, litigation concerns, extensive historical analysis needs

Selective History Migration:

• Pros: Faster migration, cleaner starting point, reduced complexity

• Cons: Limited historical reporting, potential gaps in audit trail

• Best for: Most implementations, especially when legacy system remains accessible for reference

Opening Balance Migration:

• Pros: Quickest approach, minimal data volume, clean start

• Cons: No transaction-level detail, limited historical insight

• Best for: Small businesses, companies with accessible legacy systems for historical queries

Recommended Approach:

• Master data: Complete and current

• Open transactions: All open items

• Posted transactions: Last 12-24 months

• G/L balances: Opening balances at go-live, with optional detailed history

• Archived data: Keep legacy system in read-only mode for historical reference

Migration Timing and Cutover Strategy

Strategic timing minimizes business disruption and ensures accuracy.

Cutover Timing Considerations:

Month-End/Quarter-End Cutover:

• Advantages: Clean financial period break, easier reconciliation, aligns with reporting cycles

• Challenges: Often busiest time for finance team, pressure to close quickly in both systems

Mid-Period Cutover:

• Advantages: Less time pressure, finance team availability, easier to handle issues

• Challenges: Partial-period data in each system, more complex reconciliation

Year-End Cutover:

• Advantages: Fresh start for new fiscal year, simplified reporting, clean tax year

• Challenges: Extended preparation time, holiday season complications, limited year-end support availability

Recommended Timing:

• Start of fiscal year or quarter (if business cycle permits)

• Beginning of month (second-best option)

• Avoid peak business periods (holiday seasons, fiscal year-end closing)

• Allow adequate post-cutover stabilization before period-end close

Parallel Run Strategy:

Consider running both systems temporarily:

Benefits:

• Confidence building through comparison

• Safety net for mission-critical processes

• Gradual transition reduces user stress

• Validates Business Central configuration through live data

Challenges:

• Double data entry workload

• Reconciliation complexity

• Extended timeline

• Potential user confusion

When to Use Parallel Runs:

• High-risk implementations with complex processes

• Lack of confidence in data migration quality

• User anxiety about new system

• Regulatory or contractual requirements

When to Skip Parallel Runs:

• Simple, straightforward implementations

• Strong confidence in migration quality

• Adequate sandbox testing completed

• Business cannot sustain dual-entry workload

Identifying Data Sources and Legacy Systems

Comprehensive inventory of data sources ensures nothing is missed.

Source System Assessment:

Primary Source Systems:

• Legacy ERP or accounting system

• CRM system

• Inventory management system

• Point of sale systems

• Time tracking systems

• Project management applications

• Manufacturing execution systems

Supplementary Data Sources:

• Excel spreadsheets and databases

• Email archives

• Paper records requiring manual entry

• Third-party data providers

• Legacy backup files

Source System Analysis:

For each source system, document:

• System name and version

• Data owner and administrator

• Available export formats

• Data extraction capabilities

• Data quality level

• Last updated date

• Accessibility and availability

• Retention after go-live

Data Source Challenges:

Common Issues:

• Outdated legacy systems with limited export capability

• Undocumented custom systems

• Multiple disconnected spreadsheets as "system of record"

• Tribal knowledge not documented anywhere

• Inconsistent data across different sources

• Missing data requiring reconstruction

Mitigation Strategies:

• Engage IT team early for technical extraction support

• Identify business experts who understand data relationships

• Allocate adequate time for data discovery

• Budget for data archaeology if needed

• Consider professional data extraction services for complex legacy systems

Data Cleansing and Validation Principles

Clean data is the foundation of Business Central success.

Data Quality Assessment

Begin with comprehensive quality evaluation:

Data Quality Dimensions:

Completeness:

• Required fields populated

• No missing critical information

• All related records present

• Full address information

Accuracy:

• Data reflects reality

• No obvious errors (e.g., negative quantities where illogical)

• Calculations are correct

• Dates are valid

Consistency:

• Same entity described identically across records

• Formats are standardized

• Codes follow conventions

• Related data matches

Uniqueness:

• No duplicate records

• Unique identifiers are truly unique

• Master data not replicated

Validity:

• Data conforms to Business Central constraints

• Values fall within acceptable ranges

• Relationships are valid (e.g., item exists in category)

• Codes match lookup tables

Timeliness:

• Data is current

• Obsolete records identified

• Status reflects current reality

Data Profiling Exercise:

Analyze source data systematically:

1. Volume Analysis:

   • Record counts by entity type

   • Active vs. inactive records

   • Historical date ranges

   • Growth trends

2. Field Population Analysis:

   • Percentage of records with each field populated

   • Common patterns in missing data

   • Mandatory field compliance

3. Data Distribution Analysis:

   • Value frequency distributions

   • Outlier identification

   • Pattern detection

   • Anomaly flagging

4. Relationship Analysis:

   • Orphaned records (child without parent)

   • Missing relationships

   • Referential integrity violations

Data Cleansing Strategies

Transform problematic source data into high-quality Business Central data.

Common Cleansing Activities:

Deduplication:

• Identify duplicate customer/vendor/item records

• Establish master record selection criteria

• Merge or eliminate duplicates

• Update transactional references

Standardization:

• Address formats (USPS standards, international conventions)

• Phone number formats

• Name capitalization and spelling

• Unit of measure consistency

• Code formats

Enrichment:

• Add missing required fields

• Complete partial records

• Lookup and add tax registration numbers

• Geocode addresses

• Categorize uncategorized items

Validation and Correction:

• Correct obvious errors

• Validate against external sources

• Fix calculation errors

• Resolve date inconsistencies

• Correct negative values where inappropriate

Obsolete Data Handling:

• Flag inactive customers/vendors

• Archive discontinued items

• Mark closed accounts

• Identify and segregate test data

Data Transformation:

• Convert legacy codes to Business Central conventions

• Map legacy categories to new taxonomy

• Transform data types as needed

• Restructure hierarchical relationships

Cleansing Workflow:

1. Extract source data to staging area

2. Profile to identify issues

3. Define cleansing rules and transformations

4. Execute automated cleansing where possible

5. Review exceptions and edge cases

6. Manual correction of remaining issues

7. Validate cleansed data quality

8. Document cleansing decisions and rules

Cleansing Tools:

• Excel for simple transformations

• SQL scripts for bulk operations

• Data quality tools (OpenRefine, Trifacta)

• Custom scripts (Python, PowerShell)

• Business Central data import validation

Data Mapping Methodology

Precisely define how source data maps to Business Central structures.

Data Mapping Documentation:

Create comprehensive mapping specifications:

Mapping Document Structure:

For each Business Central entity:

Entity Information:

• Business Central table name

• Import method (RapidStart, Excel, API, custom)

• Load sequence order

• Dependencies

Field Mappings:

Transformation Rules:

Document each transformation:

Lookup Tables:

Create mapping reference tables:

Example: Payment Terms Mapping

Mapping Validation:

Test mapping specifications thoroughly:

• Sample data conversion

• Edge case testing

• Null value handling

• Data type compatibility

• Length and format constraints

• Business rule validation

Master Data Migration

Establish the foundational data entities that enable transactions.

Customer and Vendor Migration

Customer Data Migration:

Customer Master Fields:

Critical Fields:

• Customer number (unique identifier)

• Name and search name

• Address information (Bill-to, Ship-to)

• Contact information (phone, email, website)

• Customer posting group

• Gen. business posting group

• VAT business posting group

• Payment terms code

• Currency code

• Credit limit

• Blocked status

Important Optional Fields:

• Salesperson code

• Customer price group

• Customer discount group

• Shipping agent

• Location code (default)

• Dimensions (default values)

• Payment method code

• Language code

Customer Contacts:

• Contact name and title

• Direct phone/email

• Role/responsibility

• Primary contact designation

Ship-to Addresses:

• Additional delivery locations

• Address details

• Contact information

• Location-specific settings

Customer Best Practices:

• Deduplicate ruthlessly—one customer, one record

• Standardize naming conventions

• Validate addresses (use address validation services)

• Complete tax registration numbers for B2B customers

• Set reasonable credit limits

• Block inactive customers appropriately

• Establish clear customer numbering convention

Vendor Data Migration:

Vendor Master Fields:

Similar structure to customers:

• Vendor number

• Name and search name

• Address and contact information

• Vendor posting group

• Payment terms and methods

• Currency code

• Default dimensions

• Tax information

Vendor-Specific Considerations:

• 1099 reporting flags (US)

• Preferred payment method

• Remittance email addresses

• Purchase from vendor restrictions

• Vendor evaluation ratings

• Insurance and license expiration dates

Item and Inventory Migration

Item Master Data:

Essential Item Fields:

• Item number (unique identifier)

• Description and description 2

• Base unit of measure

• Item category code

• Type (Inventory, Service, Non-Inventory)

• Costing method (FIFO, LIFO, Average, Standard, Specific)

Costing Method Selection Guidance:

Your choice of costing method has significant financial and operational implications:

• FIFO (First-In, First-Out):

  • Assumes oldest inventory sold first

  • Best for: Perishable goods, GAAP compliance in most countries, price trend visibility

  • Impact: Higher COGS in inflationary periods (older, cheaper inventory consumed first)

  • Note: Required or preferred in many jurisdictions

• Average Cost:

  • Recalculates average cost per unit after each purchase

  • Best for: Commodities, large volumes of similar items, stable pricing environments

  • Impact: Smooths cost fluctuations, simpler than FIFO for high-volume scenarios

  • Consideration: Business Central calculates average automatically

• Standard Cost:

  • Uses predefined cost regardless of actual purchase price

  • Best for: Manufacturing environments, budget-based costing, variance analysis

  • Impact: Requires regular standard cost updates, generates variance entries

  • Use case: Manufacturing with detailed cost accounting needs

• LIFO (Last-In, First-Out):

  • Assumes newest inventory sold first

  • Important: LIFO is NOT allowed under IFRS and illegal in many countries (including EU, Australia, others)

  • Business Central supports LIFO primarily for U.S. customers where tax benefits exist

  • Impact: Lower taxable income in inflationary periods (higher recent costs matched to revenue)

  • Declining use: Many U.S. companies moving away from LIFO due to complexity

• Specific Cost:

  • Tracks cost of each individual unit (often with lot/serial tracking)

  • Best for: High-value items, unique items, lot-specific traceability needs

  • Impact: Most accurate but administratively intensive

Migration Consideration: Changing costing methods during migration is complex. Evaluate early in requirements gathering phase and align with accounting team.

• Unit cost and unit price

• Gen. product posting group

• Inventory posting group

• VAT product posting group

• Replenishment system

• Vendor number (primary vendor)

Inventory-Specific Fields:

• Reorder point

• Reorder quantity

• Lead time calculation

• Safety stock quantity

• Lot size

• Item tracking code (for lot/serial tracking)

Manufacturing Fields (if applicable):

• Production BOM number

• Routing number

• Manufacturing policy

• Rescheduling policy

• Order tracking policy

Item Attributes and Categorization:

• Assign to appropriate item categories

• Define item attributes (color, size, etc.)

• Create item variants where needed

• Set up unit of measure conversions

Inventory Quantities:

Opening Balance Approach:

Option 1: Item Journal Entry

• Single journal entry per item per location

• Quantity and value

• Posted at cutover

• Simplest approach

Option 2: Detailed Transaction History

• Import historical inventory transactions

• Builds audit trail

• Allows lot/serial number history

• More complex but comprehensive

Inventory Validation:

• Reconcile quantities to physical counts

• Verify valuation calculations

• Validate lot/serial number assignments

• Cross-check with financial G/L balances

Item Master Best Practices:

• Standardize item descriptions and naming

• Establish clear item numbering scheme

• Eliminate obsolete and duplicate items

• Complete categorization before migration

• Validate units of measure carefully

• Determine costing method strategically

• Consider barcode requirements

Fixed Assets Migration

Fixed Asset Master Data:

Critical Fields:

• Fixed asset number

• Description and description 2

• FA class code and FA subclass code

• FA location code

• Responsible employee

• Acquisition date and cost

• Depreciation book code

• Depreciation method

• Depreciation starting date

• Number of depreciation years

• Straight-line percentage

• Ending book value

• Salvage value

Depreciation Books:

• Book-specific depreciation methods

• Tax vs. financial reporting books

• Integration to G/L flags

• Depreciation calculation parameters

Fixed Asset Best Practices:

• Verify acquisition dates and costs

• Reconcile accumulated depreciation

• Validate remaining useful life

• Set up multiple depreciation books if needed

• Assign FA locations and responsible persons

• Establish FA numbering and categorization

• Plan for ongoing depreciation calculation

Chart of Accounts Migration

G/L Account Migration:

Typically configuration rather than data migration, but may include:

• Opening balances at go-live

• Historical balance details (for trending)

• Budget data

Opening Balances:

Timing: Last closed period before go-live

Approach:

1. Extract trial balance from legacy system

2. Map legacy accounts to Business Central chart of accounts

3. Create opening balance journal

4. Include dimensions if tracking historical dimensional data

5. Balance to ensure debits equal credits

6. Post in Business Central at cutover

Historical G/L Detail:

If migrating detailed G/L history:

• Maintain transaction dates from source system

• Preserve source document numbers

• Include all dimension values

• Validate period balances match trial balances

• Consider summary entries for older periods

Using RapidStart Services and Configuration Packages

Business Central's built-in migration tools streamline data import.

RapidStart Services Overview:

RapidStart provides structured data import capabilities:

• Template-based data definition

• Excel-based data preparation

• Validation before import

• Batch import processing

• Error handling and correction

Modern API-Based Migration Approaches:

While RapidStart/Configuration Packages remain valid for simple migrations, consider modern API-based tools for larger or more complex scenarios:

Azure Data Factory (ADF):

• Cloud-based ETL (Extract, Transform, Load) service

• Pre-built connectors for Business Central APIs

• Handles large data volumes efficiently

• Supports complex transformations

• Schedule-based or event-driven migrations

• Built-in monitoring and error handling

• Best for: Large-scale migrations (100K+ records), complex legacy systems, ongoing synchronization needs

Power Automate (for simpler scenarios):

• Low-code integration platform

• Business Central connector built-in

• Good for incremental migrations

• Visual workflow designer

• Best for: Smaller datasets, incremental data loads, non-technical users

Custom AL Extensions (for specialized needs):

• AL codeunit-based migration logic

• Full control over validation and transformation

• Integration with BC business logic

• Best for: Complex business rules, unique data structures, heavy validation requirements

Tool Selection Guidance:

• Simple migrations (< 10K records, straightforward mapping): Configuration Packages / RapidStart

• Medium migrations (10K-100K records, some complexity): Power Automate or Azure Data Factory

• Complex migrations (100K+ records, complex transformations, legacy systems): Azure Data Factory + Custom AL

• Ongoing synchronization needs: Azure Data Factory or Power Automate with scheduled flows

Configuration Package Process:

1. Create Configuration Package:

• Define package code and description

• Select tables to include

• Choose fields for each table

• Set processing order

• Define validation rules

2. Export Package to Excel:

• Generate Excel template

• Pre-populated with validation rules

• Includes field descriptions

• Structured for easy data entry

3. Populate Excel Template:

• Copy/paste data from source systems

• Apply transformations

• Validate against rules

• Review for completeness

4. Import Package:

• Import Excel file back to Business Central

• System validates data

• Review and correct errors

• Apply package to import data

5. Validate Imported Data:

• Review import log

• Verify record counts

• Spot-check sample records

• Confirm relationships

Configuration Package Best Practices:

• Start with small pilot packages

• Test import in sandbox environment

• Use field mapping to handle differences

• Leverage data templates for consistency

• Import in correct sequence (masters before transactions)

• Keep original Excel files for documentation

Package Sequencing:

Proper import order prevents dependency errors:

1. General Setup (currencies, payment terms, shipping agents)

2. Posting Groups

3. Customers and Vendors

4. Items

5. Chart of Accounts

6. Open Documents

7. Opening Balances

Excel Templates and Data Import Tools

Alternative import methods for various scenarios.

Excel-Based Import Methods:

Configuration Packages (covered above): Best for structured, repeatable imports

Edit in Excel:

• Export BC data to Excel for bulk editing (via Office Add-in)

• Make changes in Excel, then publish back to Business Central

• Suitable for small-to-medium updates (hundreds of records)

• Available for supported pages (lists with Excel export enabled)

• Not true real-time sync—requires explicit publish action

CSV Import via Data Migration:

• Standard import for specific entities

• Customer and vendor CSV templates

• Item CSV templates

• Bank statement imports

API-Based Import:

• Programmatic data import

• For large volumes or complex logic

• Enables automated migration scripts

• Requires development skills

Tool Selection Criteria:

Data Migration Testing Phases

Rigorous testing ensures migration success before cutover.

Testing Approach:

Phase 1: Unit Testing

Test individual entities in isolation:

• Single customer import

• Single vendor import

• Small item set

• Sample transactions

Validation:

• Data format correctness

• Field mapping accuracy

• Default value application

• Validation rule compliance

Phase 2: Integration Testing

Test related entities together:

• Customers with ship-to addresses and contacts

• Items with inventory quantities

• Transactions with related master data

• G/L accounts with opening balances and dimensions

Validation:

• Relationship integrity

• Reference validation

• Calculation correctness

• Cross-entity consistency

Phase 3: Volume Testing

Test with production-scale data volumes:

• Full customer/vendor databases

• Complete item catalog

• All historical transactions (if migrating)

• Full G/L detail

Validation:

• Performance acceptability

• System resource utilization

• Import duration

• Data integrity at scale

Phase 4: User Acceptance Testing

Business users validate migrated data:

• Sample customer records reviewed

• Item information verified

• Open orders examined

• Financial balances confirmed

Validation:

• Business user confidence

• Data usability

• Completeness assessment

• Corrections identified

Testing Best Practices:

• Test in sandbox environment only

• Use production-like data volumes

• Document and fix errors systematically

• Retest after corrections

• Obtain formal sign-off before cutover

• Keep detailed test logs

Cutover Planning and Execution

The final migration to production requires meticulous planning.

Cutover Plan Components:

Data Privacy and GDPR Compliance Considerations:

Before migrating personal data into Business Central, address privacy and regulatory requirements:

GDPR and Data Protection:

• Data Minimization: Migrate only personal data necessary for business operations; archive or delete obsolete customer/employee records

• Consent Validation: Ensure you have legal basis to migrate personal data (contractual necessity, legitimate interest, consent)

• Right to Erasure: Implement processes to handle "right to be forgotten" requests post-migration

• Data Retention Policies: Establish and document retention policies; don't migrate data beyond legal retention requirements

Business Central Privacy Features:

• Classified Fields: Use Business Central's data classification features to tag personal/sensitive fields

• Data Subject Requests: Utilize BC's built-in tools for handling data subject access requests (DSAR)

• Audit Trail: Enable change logs for personal data modifications

• Data Encryption: Microsoft encrypts BC data at rest (Azure SQL encryption) and in transit (TLS)

Other Regulatory Considerations:

• SOX Compliance: If publicly traded (U.S.), ensure migration audit trails support SOX controls

• Industry-Specific: Healthcare (HIPAA), Financial Services (PCI-DSS), others may have specialized data handling requirements

• Data Residency: Microsoft Azure regions for BC data; ensure compliance with local data residency laws

Migration Checklist for Privacy:

1. Identify all personal data fields being migrated (names, emails, phone numbers, addresses, etc.)

2. Validate legal basis for migration under GDPR or applicable regulations

3. Anonymize or delete personal data in migrated historical transactions if not legally required

4. Document data processing activities in GDPR-required Records of Processing Activities (ROPA)

5. Train migration team on data privacy requirements

6. Engage legal/compliance team review before cutover

Pre-Cutover Activities (Days/Weeks Before):

• Final data cleansing

• Final mapping validation

• Sandbox migration rehearsal

• Cutover runbook finalization

• Go/no-go criteria establishment

• Communication to stakeholders

• Backup verification

• Resource allocation confirmation

Cutover Weekend/Period Activities:

Hour 0-2: System Freeze and Final Extraction

• Freeze legacy system for transactions

• Extract final production data

• Perform final data quality checks

• Stage data for import

Hour 2-6: Data Import

• Execute master data import

• Import open transactions

• Post opening balances

• Validate import logs

• Correct any errors

Hour 6-10: Validation and Reconciliation

• Verify record counts

• Validate key balances

• Reconcile to legacy system

• Test sample transactions

• Confirm user access

Hour 10-12: Go-Live Preparation

• Final smoke tests

• User notification

• Enable production access

• Begin monitoring

Post-Cutover Activities:

• Intensive user support (hypercare)

• Transaction monitoring

• Issue tracking and resolution

• Daily reconciliation

• Legacy system archival (after stabilization)

Cutover Runbook:

Detailed, step-by-step instructions including:

• Each activity with estimated duration

• Responsible person

• Prerequisites

• Specific commands or procedures

• Validation checkpoints

• Rollback procedures (if needed)

• Communication protocols

• Escalation contacts

Example Runbook Entry:

Handling Opening Balances

Opening balances establish your starting point in Business Central.

Financial Opening Balances:

Trial Balance Import:

• Extract final trial balance from legacy system

• Map to Business Central chart of accounts

• Include all dimension values

• Create general journal entry

• Balance debits and credits

• Post at go-live date

Customer Opening Balances:

Two approaches:

Approach 1: Detail Level

• Import each open invoice

• Maintains full transaction history

• Enables aging reports

• Preserves due dates and terms

Approach 2: Summary Level

• Single balance per customer

• Faster import

• Less detailed history

• Suitable when detail isn't required

Vendor Opening Balances:

Similar approach options:

• Detailed: Import each open bill

• Summary: Single balance per vendor

Inventory Opening Balances:

• Quantity on hand by item and location

• Inventory value

• Lot/serial number details (if tracking)

• Posted via item journal

Opening Balance Best Practices:

• Balance to legacy system trial balance

• Verify customer and vendor balances with statements

• Perform physical inventory count for quantities

• Document opening balance date clearly

• Retain backup of legacy balances for reference

• Reconcile to tax returns and statutory filings

Data Archival Strategies for Legacy Data

Plan for long-term access to historical data not migrated.

Legacy System Retention:

Read-Only Access:

• Maintain legacy system in read-only mode

• Enable queries for historical research

• Keep available for audits

• Define retention period

Data Export and Archival:

• Export complete database to neutral format

• Store in accessible location

• Document data structure and relationships

• Include user guide for data access

Reporting Archive:

• Generate and save key historical reports

• Export to PDF for long-term retention

• Index for easy retrieval

• Include financial statements, tax returns, audit reports

Archival Timeline:

Immediate (Go-Live):

• Legacy system in read-only mode

• Full backup created

Short-Term (3-6 months):

• Verify Business Central stability

• Confirm no critical data gaps

• Keep legacy system easily accessible

Medium-Term (6-12 months):

• Archive to offline storage

• Decommission legacy system

• Establish data request process

Long-Term (1+ years):

• Archived data on secure storage

• Annual access verification

• Compliance with retention policies

Archival Best Practices:

• Comply with legal and regulatory retention requirements

• Maintain data readability (avoid obsolete formats)

• Document archive contents and access procedures

• Test data restoration periodically

• Secure archived data appropriately

Frequently Asked Questions (FAQ)

How do you migrate data to Business Central?

Business Central data migration follows a structured 8-step methodology:

Step 1: Data Assessment

• Inventory all source systems (ERP, CRM, spreadsheets, databases)

• Analyze data volume: Record counts for customers, vendors, items, transactions

• Evaluate data quality: Completeness, accuracy, consistency, duplicates

• Identify data cleansing requirements

• Document business rules and transformations

Step 2: Define Migration Scope

Decide what to migrate:

• Master Data (Always):

  • Customers, vendors, items

  • Chart of accounts

  • Fixed assets, employees

  • Price lists, payment terms

• Open Transactions (Required):

  • Open sales orders, purchase orders

  • Unpaid customer/vendor invoices

  • Open bank transactions

• Historical Data (Selective):

  • Option A: Last 12-24 months of transactions

  • Option B: Opening balances only (keep legacy system for history)

  • Option C: Full history (rare—regulated industries only)

Step 3: Select Migration Tools

For Small-Medium Datasets (<10,000 records):

• Excel Configuration Packages: Business Central's built-in tool

  • Download template from BC

  • Populate Excel with source data

  • Import and post in BC

  • Best for: Master data, opening balances

For Large Datasets (>10,000 records):

• Azure Data Factory: ETL platform for complex transformations

• Power Automate: Automated data flows using BC connectors

• Third-Party Tools: RapidStart, Scribe, KingswaySoft

For Complex Scenarios:

• Custom AL Extensions: Programmatic migration via AL code

• APIs (OData/REST): Direct API calls for real-time migration

Step 4: Create Data Mapping

Document field-level transformations:

Step 5: Data Cleansing

Clean legacy data before migration:

• Duplicates: Merge duplicate customer/vendor/item records

• Standardization: Consistent formats for phone numbers, addresses, names

• Validation: Complete required fields (addresses, tax IDs, payment terms)

• Accuracy: Verify balances, quantities, pricing

• Currency: Convert multi-currency if needed

Step 6: Test Migration (Iterative)

• Test 1: Unit testing with sample data (50-100 records)

• Test 2: Volume testing with full dataset in Sandbox

• Test 3: Integration testing (create transactions, post to G/L)

• Test 4: User Acceptance Testing and sign-off

Step 7: Cutover Execution (Go-Live Weekend)

1. Freeze legacy system (read-only or full freeze)

2. Extract final data from source systems

3. Load data into Production Business Central

4. Run validation reports

5. Reconcile to legacy system

6. Go/No-Go decision

Step 8: Post-Migration Validation

• Reconcile opening balances (A/R, A/P, inventory, G/L)

• Verify customer/vendor aging

• Test transaction posting

• Daily reconciliation for first week

What tools are used for Business Central data migration?

Business Central supports multiple migration tool options:

1. Excel Configuration Packages (Built-In, FREE)

Best For: Small-medium datasets (<10,000 records per entity)

Advantages:

• ✅ No additional cost (included in BC)

• ✅ Easy to use (Excel-based)

• ✅ Good for master data and opening balances

• ✅ Built-in validation

Limitations:

• ❌ Slow for large datasets

• ❌ Limited transformation capabilities

• ❌ Manual process (not automated)

2. Azure Data Factory (Microsoft ETL Platform)

Best For: Large datasets, complex transformations, multi-source migration

Advantages:

• ✅ Handles millions of records

• ✅ Complex transformations (joins, aggregations, lookups)

• ✅ Automated and scheduled

• ✅ Incremental loads

• ✅ Logging and monitoring

Limitations:

• ❌ Requires Azure subscription (cost: ~$1-2/hour)

• ❌ Technical complexity

• ❌ Setup time (2-4 weeks)

3. Power Automate (Low-Code Automation)

Best For: Moderate datasets, ongoing integrations, API-based migration

Advantages:

• ✅ Low-code (no programming required)

• ✅ Pre-built BC connectors

• ✅ Good for incremental migrations

Limitations:

• ❌ Flow execution limits (5,000 actions/day)

• ❌ Slower than ADF for bulk loads

4. Custom AL Extensions (Code-Based)

Best For: Highly customized data, complex business logic

Advantages:

• ✅ Full control over migration logic

• ✅ Complex validations and transformations

• ✅ Reusable for future migrations

Limitations:

• ❌ Development cost ($20K-$50K)

• ❌ Requires AL developer expertise

• ❌ Maintenance overhead

Tool Selection Guide:

How long does Business Central data migration take?

Data migration timelines vary based on data volume, quality, and complexity:

Typical Timeline (3-6 weeks total):

Week 1: Assessment & Planning

• Data inventory and volume analysis

• Migration scope definition

• Tool selection

• Project plan creation

Week 2: Mapping & Preparation

• Field-level mapping

• Transformation rules documentation

• Data cleansing in source systems

• Migration tool setup

Weeks 3-4: Testing & Refinement

• Test 1: Sample data migration

• Fix mapping errors

• Test 2: Full volume migration to Sandbox

• Test 3: Integration testing

• User acceptance testing

Week 5: Cutover Preparation

• Cutover runbook finalization

• Rollback plan development

• Final data cleansing

• Dress rehearsal

Week 6: Cutover & Validation

• Friday PM: Freeze legacy system

• Saturday: Execute production migration

• Sunday: Validation and reconciliation

• Monday: Go-Live

Timeline by Organization Size:

Small (1 location, <10K records): 3-4 weeks Medium (2-5 locations, 10K-100K records): 5-8 weeks Large (Multi-location, >100K records): 10-16 weeks

Factors That Extend Timeline:

• 🔴 Poor source data quality (adds 2-4 weeks)

• 🔴 Multiple legacy systems (adds 1-2 weeks each)

• 🔴 Complex transformations (adds 2-3 weeks)

• 🔴 Full historical data migration (adds 3-6 weeks)

Factors That Accelerate:

• 🟢 Clean, well-structured source data

• 🟢 Single source system

• 🟢 Opening balances only (no history)

• 🟢 Experienced migration team

What is GDPR compliance for Business Central data migration?

GDPR (General Data Protection Regulation) requires specific data privacy protections:

GDPR Principles Affecting Migration:

1. Data Minimization

• Only migrate necessary business data

• Don't migrate dormant records (inactive >5 years)

• Archive rather than migrate old employee records

2. Right to Be Forgotten

• Honor deletion requests before migration

• Exclude deleted individuals from migration

• Implement BC's "GDPR Deletion" functionality

3. Cross-Border Data Transfer

• EU data must stay within EU

• Business Central Online: Choose EU data residency in Admin Center

• Migration Tools: Ensure GDPR-compliant processing (e.g., EU-hosted ADF runtime)

4. Data Encryption

• Use HTTPS/TLS for API-based migrations

• Encrypt data files during transfer

• Business Central Online: Automatic encryption at rest

5. Access Controls

• Limit migration team access to minimum necessary

• Use BC permission sets (don't grant SUPER to everyone)

• Remove temporary migration accounts post-go-live

GDPR Migration Checklist:

Pre-Migration:

• Data Protection Impact Assessment completed

• Lawful basis documented for each data category

• Deletion requests honored

• Data minimization applied

• EU data residency configured

During Migration:

• Encrypted transfer methods used

• Access restricted to authorized team only

• Migration tools GDPR-compliant

• Audit logging enabled

Post-Migration:

• Personal data fields verified

• BC GDPR features configured

• Data retention policies set

• Legacy system data securely destroyed

Business Central GDPR Features:

1. Data Classification: Tag personal data fields (Personal, Sensitive)

2. Customer Consent: Track marketing consent

3. GDPR Deletion: Delete customer/contact data on request

4. Retention Policies: Automatic deletion of old records

5. Audit Trails: Track access/modifications to personal data

Penalties: Up to €20 million or 4% of global annual revenue

Deliverables: Data Migration Phase Outputs

Complete this phase with comprehensive data successfully migrated:

1. Data Migration Plan

Comprehensive plan including:

• Migration scope and approach

• Source system inventory

• Cutover strategy and timing

• Resource requirements

• Risk mitigation strategies

2. Data Mapping Worksheets

Detailed field-level mapping:

• Source to target mapping

• Transformation rules

• Validation rules

• Lookup tables

3. Validation Checklist

Testing and validation criteria:

• Unit test cases

• Integration test cases

• Volume test results

• UAT sign-off

4. Cutover Runbook

Step-by-step execution guide:

• Pre-cutover activities

• Cutover procedures

• Validation checkpoints

• Rollback procedures

Conclusion: From Legacy to Business Central

Data migration is the bridge that carries your business history into your Business Central future. While challenging, a methodical approach centered on data quality, comprehensive testing, and careful execution delivers clean, reliable data that empowers confident business operations in your new system.

Key Takeaways:

✓ Plan Strategically: Balance migration completeness with practicality—not all data deserves migration

✓ Cleanse Thoroughly: Invest in data quality—garbage in becomes garbage out

✓ Map Precisely: Document every transformation clearly

✓ Test Rigorously: Multiple testing phases catch issues before production impact

✓ Execute Methodically: Follow your cutover runbook precisely

✓ Validate Extensively: Reconcile everything to legacy system

With clean, accurate data successfully migrated into your configured Business Central environment, you're positioned for the next phase: Customization, Extensions & Integration, where you'll extend Business Central capabilities to meet unique business requirements.

Next in Series: Blog 5: Customization, Extensions & Integration - Learn how to extend Business Central through custom development, AppSource extensions, and integrations with other systems.

Download Resources:

• Data Migration Plan Template

• Data Mapping Worksheet

• Cutover Runbook Template

• Validation Checklist

Questions or Comments? Share your data migration experiences and lessons learned in the comments below.

This is Part 4 of an 8-part series on Business Central Implementation. Subscribe to receive notifications when new articles are published.

Tags: #BusinessCentral #DataMigration #ERPImplementation #DataQuality #Dynamics365 #MigrationStrategy