Data migration carries several risks that can negatively affect the functionality of the new system or lead to the loss of important information. Among the most common risks is a poorly designed migration process — unclear or incorrect rules that define what data should be transferred and how.
Another frequent issue involves lost or incomplete data, which may not be transferred at all due to technical errors or incorrect data mapping. A significant risk is low data quality — outdated, duplicate, or erroneous records that haven’t been cleaned before the migration. Lastly, data transformation errors can occur, where data is misinterpreted or doesn’t meet user expectations after the transfer.
These risks can be significantly reduced through thorough analysis, testing, and continuous validation throughout the entire migration process.
How to Prepare for Data Migration
To minimize the risks associated with data migration, it is essential to approach the process systematically, with a strong focus on preparation, data quality, and overall data governance. Key recommendations for mitigating risks during data migration include:
- Thorough Analysis and Migration Design
Before the migration begins, it is essential to clearly define which data will be migrated, how it will be transformed and mapped, and what the specifics of the target system are. A poorly designed migration is a common source of errors, which is why data architects, business analysts, and IT specialists must be involved in the process from the start. - Ensuring High Data Quality Before Migration
One of the most important factors for a successful migration is the quality of existing data. Poor-quality data — such as duplicates, inconsistencies, or outdated records — will be carried over into the new system, potentially causing:- errors in reporting,
- malfunctioning processes,
- user dissatisfaction,
- or even system failure.
- Focus on Master Data Management (MDM)
Master data (such as customers, products, vendors, or locations) forms the backbone of many business processes. Therefore, it’s important to:- clearly define responsibilities for their governance,
- implement standards and rules for their creation and maintenance,
- and ensure consistency across systems.
Well-governed master data reduces the risk of errors, redundancy, and ultimately poor decisions in the new environment.
- Testing and Validation at Every Stage
Testing should be part of every migration phase — from pilot migrations and integration tests to final validations.
We recommend using automated validation scripts to compare original and migrated data. - Backup and Recovery Plan
Before every migration, there should be a reliable backup of the original data and a clearly defined recovery plan in case the migration fails. - Business Involvement and Communication
Close collaboration between the IT team and end users is critical. Users often have the best knowledge of the data specifics, business processes, and expectations, and should therefore be involved in both the design and testing phases.
Conclusion
Data migration is not just a technical project — it is a transformational process that reveals both the strengths and weaknesses of an organization’s data management practices. Effective master data management (MDM) and a strong focus on data quality are critical prerequisites for a successful migration that delivers the expected benefits.
