Data Migration Examples - Safe Transitions to New Systems

Data migration is one of the most underestimated components of any system replacement or cloud transition. Many organisations invest months selecting a new platform but allocate too little time for safely and completely transferring their existing data. The consequence: delays, incomplete datasets, and in the worst case, permanent data loss. A careful migration approach with validated ETL pipelines, thorough testing, and a clear rollback strategy makes the difference between a smooth transition and a costly failure. At MG Software, we treat data migration as a full project with its own planning, test cycles, and acceptance criteria, not as an afterthought handled at the last moment. We begin every migration with an extensive data audit that maps the volume, quality, and dependencies of the source data. Below we share three migration projects, each with a unique starting point and target system. Each example illustrates the technical complexity, risks, and strategic decisions involved in a successful data migration.

Legacy ERP to cloud-based system

A manufacturing company with 180 employees migrated from a 15-year-old on-premise SAP R/3 system to a modern cloud-based ERP platform. The biggest challenge was source data quality: over fifteen years, thousands of inconsistent records had accumulated, from duplicate supplier entries to fields repurposed for unintended uses. Prior to the migration, we conducted an extensive data cleaning phase that identified and merged 23,000 duplicate records. The actual migration of over 2 million records was executed via a custom ETL pipeline in Python that transformed, validated, and wrote data in batches of 10,000 records. Each batch was verified with checksums and row counts to detect data loss. The cutover was performed during a planned weekend with a parallel test environment as fallback, and on Monday the company was running entirely on the new system without a single hour of unexpected downtime.

• Pre-migration data cleaning phase identifying and merging 23,000 duplicate records
• Custom ETL pipeline in Python transforming and validating data in batches of 10,000 records
• Automatic data validation with checksums and row counts after each migration batch
• Rollback strategy with parallel test environment and point-in-time recovery capability
• Phased migration that only decommissions the production system after full validation of the target
• Zero unexpected downtime: cutover executed during planned weekend with full production on Monday

Multi-database consolidation for merger

Following a merger between two mid-size consulting firms, three different customer databases needed to be consolidated into one unified platform. One firm used Salesforce, the other ran a custom-built CRM in PostgreSQL, and there was also an Excel file with 4,000 contacts from joint projects. The core challenge was deduplication: the same customer could exist in all three sources with different spellings, phone numbers, and address formats. We developed a fuzzy matching algorithm that compares records on name, email address, chamber of commerce number, and address with configurable weighting per field. The algorithm identified 8,200 potential duplicates, of which 7,100 were automatically merged and 1,100 were manually reviewed by the project team. All data formats were standardised to a uniform model with validated phone number formats, normalised addresses, and consistent company names. The complete transformation history is recorded in an audit trail that makes it traceable which source records were combined for each merged record.

• Fuzzy matching algorithm with configurable per-field weighting for duplicate customer record detection
• Automatic merging of 7,100 duplicates and manual review of 1,100 borderline cases
• Standardised data formats including phone, address, and company name normalisation
• Migration from three heterogeneous sources: Salesforce, custom PostgreSQL CRM, and Excel
• Complete audit trail showing source records and transformations for each merged record
• Validation interface for the project team to approve or reject borderline cases with one click

E-commerce platform migration with SEO preservation

A web shop with 12,000 products and a monthly revenue exceeding 400,000 euros migrated from WooCommerce to a headless e-commerce architecture with Shopify as the backend and a Next.js storefront. Beyond the technical product data migration, preserving organic search traffic was the biggest concern: the shop generated 60% of its revenue through Google and could not afford any ranking loss. All products, categories, customer accounts, and three years of order history were transferred via a custom migration script that transformed WooCommerce exports to the Shopify product model. An automated URL mapping linked every old WooCommerce URL to its new Shopify equivalent, resulting in over 14,000 301 redirects. After migration, we ran an automated validation checking every product page for correct metadata, working images, and accurate pricing. Three months post-migration, Google Search Console showed organic traffic had actually increased by 8% rather than declined, partly because the faster Next.js frontend significantly improved Core Web Vitals scores.

• URL mapping and 14,000+ 301 redirects for complete preservation of SEO value and organic traffic
• Custom migration script transforming WooCommerce exports to the Shopify product model
• Product data transformation including variants, metadata, images, and customer reviews
• Automated post-migration validation of metadata, images, and pricing on all product pages
• Migration of customer accounts and three years of order history for a seamless customer experience
• Organic traffic increased 8% three months post-migration thanks to improved Core Web Vitals

Key takeaways

• A phased migration strategy with parallel systems reduces risk and allows rollback if problems occur during the transition.
• Automatic data validation after each batch is essential to guarantee data integrity and detect errors before they propagate.
• For e-commerce migrations, SEO preservation is crucial: plan URL mappings and redirects before migration to retain organic traffic.
• Invest in a data cleaning phase before migration to prevent polluted data from ending up in the new system.
• Fuzzy matching algorithms are indispensable for database consolidations to detect duplicate records despite spelling variations.
• A complete audit trail of all transformations is useful not only for debugging but also required for compliance in regulated industries.
• Run the migration multiple times in a staging environment before migrating production data so the team is familiar with the process.
• Schedule the cutover during a low-activity period and communicate clearly in advance to all involved teams and end users.

How MG Software can help

MG Software guides complex data migrations from the initial data audit through to the final validation checks after go-live. Our process starts with a thorough analysis of your source data, mapping volume, quality, dependencies, and risks. Based on this analysis, we develop a migration plan with a timeline, rollback strategy, and acceptance criteria signed off by all stakeholders. We build custom ETL pipelines that transform, validate, and load data into the target system, including automatic checks after each step. Every migration project is rehearsed multiple times in a staging environment before production data is migrated. Timelines range from 3 weeks for straightforward migrations to 4 months for enterprise projects involving multiple source systems and complex data transformations.