Data migration

A recent study by Experian shows that 9 out of 10 companies are engaged in data migration projects and 85% of them are facing significant problems (cost or budget overruns) during their implementation.

Other situations are more difficult to count but have a major impact on the perceived quality of a new system. One example is poorly migrated data that gives the impression that the new system has problems (bugs). The impact here can be for both internal users and customers; This will result in costs related to customer satisfaction or attrition.

There are myths about a migration project that contribute to these problems. For example: A migration is only an export of the source and an import into the target or let's start from what we have in the source and bring it into the target.

A migration is an activity that requires analysis and planning. These activities as well as the realization of the project involve your resources but as a source of information and not as a conductor of the project. Migration projects are not part of day-to-day operations and are not documented in standard business process manuals. Best practices, validation algorithms, and reliability tools may not be part of your IT department's toolbox.

For almost 30 years Softconcept has done dozens of mandates in a variety of environments and constraints that can not be recreated in a single organization.

The development of a rigorous and flexible methodology assure the migration, consolidation and transfer of environments to be efficient, effective and predictable.

Phase 1: Initial Assessment

The initial assessment makes it possible to specify the following elements: the project scope, the approach used, the project team, the stakeholders, the technical environments, the committees, the loads, the planning, the deliverables schedule and the tools needed in place to manage the different phases of the project.

Phase 2: Analysis (Specifications)

The main purpose of specifying migration rules is to analyze field by field, source and target data to define migration rules (ETL).

Phase 3: Implementation (Unit tests and integration)

The implementation phase should develop the programs (or queries) and tools needed to migrate data from the source system to the target system.

Phase 4: Static Certification

The Static Certification objectives are to validate the data migration and reliability tasks and also to identify the certification controls that will be performed during the commutation.

Phase 5: Dynamic Certification

Dynamic Certification is executed once the applications platform has been qualified / certified and the data has been certified correct. Dynamic Certification consists of rolling out test scenarios to simulate the operation of the target platform with migrated data.

Phase 6: Commutation and Follow-up

The Commutation is the process that allows, when the time comes, to move from the old Information System to the new Information System.

Reliability of data

Data migration is an opportunity to provide insight into the quality of Source Information System data, as the migration rules define how they will be resumed, specifying their format and content. For example, you can define that a datum is numeric and find that it contains alphabetic characters. In view of this, an analysis of the source data must be performed to verify the adequacy of the data with the migration rules. This analysis is done as early as possible as part of the data migration work (data audit). The anomalies detected are then transmitted to a specific site called "Reliability of data". For example: the structuring, standardization and validation of addresses.

To learn more about the details of the phases of the migration, download our publication.