Data Migration
The process of moving data between storage systems, databases, formats or applications – e.g. during system changes, cloud migrations or mergers.
Data migration is one of the most critical IT processes: data is a company’s most valuable asset, and a faulty transfer can seriously disrupt operations. Whether it’s a system change, cloud migration, acquisition or consolidation – a well-planned and executed migration ensures all data arrives in the new system completely, correctly and securely. Gartner reports that over 80% of data migrations fail due to inadequate planning.
What is Data Migration?
Data migration is the full process of moving data from a source system to a target system. It is more than copying: data must be analysed, cleansed, transformed (mapping, format conversion, enrichment) and validated. Types include storage migration (change of medium), database migration (e.g. Oracle to PostgreSQL), application migration (e.g. SAP R/3 to S/4HANA) and cloud migration (on-premise to cloud). The ETL process (Extract, Transform, Load) is the common pattern: data is extracted, transformed and loaded into the target.
How does Data Migration work?
A migration typically has five phases: 1) Analysis – capture source and target models, check data quality, define mapping rules. 2) Planning – choose strategy (big bang vs. phased), schedule, rollback plan and responsibilities. 3) Design & build – develop ETL pipelines, implement transformations, write validation scripts. 4) Test migration – several dry runs with real data, validate results, performance tests. 5) Go-live – run production migration, post-migration validation, monitor new systems and keep rollback ready.
Practical Examples
A mid-size company migrates its customer database from an old Access system to PostgreSQL, including cleansing 15 years of duplicates.
An e-commerce company migrates its shop from Magento 1 to Shopify – 500,000 products, order history and customer data.
Two merged companies consolidate CRMs: customer master data from Salesforce and HubSpot into one system.
A hospital migrates patient records from a legacy system to a new electronic health record under strict GDPR and medical documentation rules.
A SaaS provider migrates its multi-tenant database from MySQL to PostgreSQL with zero-downtime using dual-write and shadow reads.
Typical Use Cases
System change: Move all data when replacing legacy systems with modern software
Cloud migration: Move databases and file stores from on-premise to cloud
Mergers & acquisitions: Combine data from different systems of two companies
Database modernization: Change database technology (e.g. Oracle to PostgreSQL, MongoDB to DynamoDB)
Compliance: Move data into certified environments for GDPR or data sovereignty
Advantages and Disadvantages
Advantages
- Enables modernization: Without migration, companies stay locked into old systems
- Data quality: The process is a chance to cleanse and standardize data
- Long-term cost savings: Modern systems are often cheaper to run
- Consolidation: Fragmented silos become one consistent data base
- Compliance: Data can be moved into compliant environments
Disadvantages
- High risk: Failed migrations can cause data loss or corruption in the target
- Time: Complex migrations often take months including analysis, tests and rework
- Downtime: Big-bang migrations require the source system to be down during cutover
- Hidden complexity: Data quality issues and undocumented dependencies often appear only during migration
Frequently Asked Questions about Data Migration
What is the difference between big-bang and phased migration?
How do I ensure data quality during migration?
How long does a typical data migration take?
Related Terms
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