Background:
Data migration is a critical phase of large IT projects, especially when transitioning from legacy information systems to new solutions. Errors in formalizing requirements can lead to the loss of important information or prolonged downtime when launching a new system (most commonly in large-scale implementations). History has sad cases of banks collapsing due to failures in migration!
Problem:
Unclear formats of sources and destinations, differences in data structure, incompatibility of reference books, errors in mapping, and conflicting expectations between the business team and the technical integrator are the main reasons for problematic migrations. Fictional or insufficiently detailed documentation leads to the inability to track which data is critical, which can be aggregated, and which cannot be moved at all.
Solution:
The system analyst conducts a data audit on the source side together with the owners of the business processes; creates a detailed data mapping, determining which attributes are subject to mandatory migration and which are not. Documents assumptions about data quality (completeness, accuracy), defines transformation rules (e.g., changing date formats, currencies, encodings). For critical fields, a sample test migration and reconciliation scenarios are implemented. At each stage, rollback instructions and migration success criteria are documented simultaneously.
Key Features:
Is it enough to just check the data formats between systems for successful migration?
No. Even with matching formats, there can be mismatches in the business sense of data, the validity of reference books, and the semantics of values. A business and technical audit of each field is necessary.
Can we limit ourselves to migrating only the "most demanded" data?
No. "Rare" data may be business-critical for specific user processes (e.g., for old contracts, insurances). Everything must be documented in requirements.
Should the possibility of rollback migration be considered?
Absolutely — even with 100% testing, critical errors may occur after go-live; rollbacks are a mandatory part of the documentation.
Negative Case: During the migration of a legacy banking system, the analyst agreed only to transfer the main tables without clarifying the business meaning of the old unused reference books. As a result, clients encountered issues with transaction histories from previous years after the launch.
Pros:
Cons:
Positive Case:
The analyst, together with the business and IT, conducted a review of all datasets, documented separate requirements for transferring "rare" data, and agreed upon validation scenarios and rollback. The migration was successful, and no critical complaints arose.
Pros:
Cons: