Data Migration: Process, Types, and Golden Rules to Follow

What is data migration?

What makes companies migrate their data assets.
  • legacy software modernization or replacement,
  • the expansion of system and storage capacities,
  • the introduction of an additional system working alongside the existing application,
  • the shift to a centralized database to eliminate data silos and achieve interoperability,
  • moving IT infrastructure to the cloud, or
  • merger and acquisition (M&A) activities when IT landscapes must be consolidated into a single system.

Data migration vs data integration

Data migration vs data replication

Main types of data migration

Six major types of data migration.Six major types of data migration.

Storage migration

  • from paper to digital documents,
  • from hard disk drives (HDDs) to faster and more durable solid-state drives (SSDs), or
  • from mainframe computers to cloud storage.
Many big enterprises still rely on mainframes to run their business processes. Source: TechRepublic

Database migration

  • an upgrade to the latest version of DBMS (so-called homogeneous migration),
  • a switch to a new DBMS from a different provider — for example, from MySQL to PostgreSQL or from Oracle to MSSQL (so-called heterogeneous migration)

Application migration

Data center migration

Business process migration

Cloud migration

Approaches to data migration

Big bang data migration

Trickle data migration

Data migration process

  • planning,
  • data auditing and profiling,
  • data backup,
  • migration design,
  • execution,
  • testing, and
  • post-migration audit.
Key phases of the data migration process.

Planning: create a data migration plan and stick to it

Data auditing and profiling: employ digital tools

Data backup: protect your content before moving it

Migration design: hire an ETL specialist

Execution: focus on business goals and customer satisfaction

Data migration testing: check data quality across phases

Post-migration audit: validate results with key clients

Golden rules of data migration

  • Use data migration as an opportunity to reveal and fix data quality issues. Set high standards to improve data and metadata as you migrate them.
  • Hire data migration specialists and assign a dedicated migration team to run the project.
  • Minimize the amount of data to be migrated.
  • Profile all source data before writing mapping scripts.
  • Allocate considerable time to the design phase as it has a high impact on project success.
  • Don’t be in a hurry to switch off the old platform. Sometimes, the first attempt of data migration fails, demanding rollback and another try.

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