What is data migration?

At a glance

• Common types: storage migration, database migration, application migration, cloud migration and business process migration.
• Requires careful planning, data cleansing and validation.
• Goal: data integrity, consistency and quality in the target system.

Data migration definition

Data migration refers to the process in which data is transferred from one system, storage location or format to another. Such projects often become necessary when companies introduce new technologies, modernize existing IT infrastructures or migrate data from local systems to cloud environments. This is not just about the physical transfer, but also about ensuring data integrity, consistency and quality in order to make sure that the data arrives correctly in the target system.

An error-free data migration is therefore decisive for continuing business operations without interruptions and for creating the foundation for successful digitalization and future analyses. A central success factor here is careful planning and validation.

What types of data migration are there?

Data migration can be divided into various types depending on the use case and objective. Each type of data migration requires specific strategies and tools to ensure a smooth implementation. The most common types are:

• Storage migration: Here, data is transferred from one storage location to another, e.g. from local servers to a cloud. The goal is often to improve storage capacity, speed or security.
• Database migration: With this type, data is moved from one database platform to another or to a new database management system. This often involves data conversions in order to support new data formats or models and thus benefit from more modern technologies or better analysis functions.
• Application migration: Here, applications and their data are relocated to a new environment, e.g. from a local infrastructure to the cloud. Storage migrations and database migrations can be combined in the process.
• Cloud migration: Data, applications or entire systems are transferred from local data centers to the cloud or between cloud environments. Companies benefit from more flexibility, scalability and cost efficiency.
• Business process migration: Here, not only data but also the applications, workflows and associated business processes are adapted and migrated to a new system. This often serves to optimize, consolidate or adapt to new market requirements.

What are the phases of data migration?

A successful data migration requires clear structuring of the process to ensure data integrity and business continuity. The typical steps of a data migration are:

• 1. Planning and analysis: First, the data to be migrated is identified, its quality is analyzed, and the requirements of the target system are defined. In addition, the goals of the project, responsibilities, the appropriate tools, and the timeframe should be defined. A migration strategy—that is, big bang or incremental migration—should also be defined.
• 2. Data extraction: Data is extracted from the existing systems, taking structure and integrity into account.
• 3. Transformation and cleansing: The data is converted into the format of the target system, with structural adjustments being made and duplicates, inconsistencies, and errors being cleaned up.
• 4. Testing and validation: Test runs are carried out to ensure that the transformed data is transferred correctly and meets the business requirements.
• 5. Migration and integration: If nothing stands in the way of an error-free process, the validated data is transferred into the target system.
• 6. Decommissioning and monitoring: Finally, after a successful data migration and the implementation of processes for continuous data monitoring, the old systems can be shut down.

What challenges does data migration entail?

Data migration is a complex process associated with various risks and difficulties. These can be both technical and organizational in nature and require careful planning as well as suitable measures to minimize risks. The most common challenges are:

Data migration vs. data conversion vs. data integration

The terms data migration, data conversion, and data integration sound similar, but they describe different processes in handling data:

• Data migration involves the transfer of data from one system, storage location, or format to another. This is often a one-time process in which large volumes of data are moved from a legacy system into a new environment, often a cloud-based solution. The focus is on the physical and structural transfer of the data.
• Data conversion is a specific subprocess of migration in which data is converted from one format into another. This may be necessary when data has to be transferred from an older version of an application to a new format. The conversion generally involves adapting the data structure so that it can be used in the new system, and it can also include data cleansing and enrichment.
• Data integration aims to combine and seamlessly consolidate data from various sources and enable a unified view of it. This is typical of scenarios such as business intelligence or data analysis, in which consolidated data sets from multiple systems are required.

In summary: data migration means moving data between systems, while data conversion adapts the formats and data integration links data from various sources for a unified view.

Application examples: where is data migration used?

Data migration occurs in almost every larger IT and digitalization project. The following examples show typical fields of application in companies.

Summary

Data migration is far more than a technical transfer: it is a critical process that determines the quality of the data base in the new system – and thus the reliability of future analyses and decisions. Careful planning, data cleansing and validation are decisive for success. With DeltaMaster and its consulting expertise, Bissantz accompanies companies through this process – from the design to the productive use of the migrated data.