Data migration: Data security
Data must be protected from unauthorized access throughout the data conversion lifecycle.
Access to data during the production transition will be limited to those involved in the run to avoid untraceable changes to the data.
Primary participants in the data conversion process should have temporary rights to perform activities in production, during switchover and after go live. In order to provide timely execution and support.
The key resources to the data conversion process should have extended rights to perform activities in the development systems, during the analysis and construction phase to ensure that the tests can be executed without being modified by the resources unavailable.
A list should be created listing all the participants who had access to each system with a clear list of what access they have.
Any request for access must follow the request procedure in effect for each system at the time of the request.
There will be separate security documents, which refer to the approved list of data objects and attributes of each entity and security indicators of each or identified. This identification is the basis of all data protection and masking activities for loading data before loading into production.
Data migration: Cross-reference management
Cross references should only be used to manage the conversion of data from source to target; They should not be used during continuous data interfacing.
- Any cross-reference that will be used in a current interface must receive sponsorship from leaders;
- A defined owner to maintain cross-reference content for release and post-launch efforts;
All cross-references will need to be supported by a single cross-reference solution created centrally and managed by the enterprise data team.
Any mapping requiring cross-referencing should be well socialized through governance within the program to ensure that all participants have a common understanding of the change and can independently assess the implications.
Data migration: Data transformation
Any data conversion that requires internal match and merge collapse capabilities should have these functions fully automated in order to limit the amount of human interaction required while performing the conversion.
All calculations, defaults, and benchmarks should be based on information captured as part of the attribute management process (metadata).
Data conversion transformation routines should be examined for reuse capability; those that have conversion or interface applications should be taken from the data conversion so that they can be reused as is for future reference.
Data migration: Data reconciliation
All data conversions should be designed so that there is a clear link from source to target and from target section to source that the data can be fully reconciled without further research being required.
All reconciliation documents should be designed with the end user so that they can be used by the end user effectively and independently to assess the completeness of the data conversion.
Data migration: Education and training
The business team should organize a training and launch for all functional data analysts, before the start of a wave (wave: module) of data migration.
An education plan will need to be created to ensure that all participants involved in the execution of this strategy and approach, will understand their particular role and how it interacts with other roles within the methodology.
Data migration: Communication
This is a deliverable from our analysts for communication via users. A communication plan will be needed to overcome blockages early on in the data conversion process and continue to be updated throughout the process.
The communication plan will be clearly defined:
- The participants involved;
- How to communicate with them by email or phone;
- The expected data details (summary, type of fields, type of data, etc.);
- When to communicate with them based on their roles and responsibilities;
Data migration: Testing and execution
Test environments should be configured such that there is a stand-alone environment to perform repetitive testing of data conversions before being required in the test environment for process components.
All test environments and associated processes should be configured to create refresh / restore points, allow data to be restored or deleted in order to handle improperly loaded data.
Data migration: Decision making
Any data conversion should have a decision maker identified for their commitment to the success of the overall program and have allocated sufficient time in their schedule to be available to answer questions.
The managers making the decisions must be sufficiently involved in the program in order to make a decision regarding the following:
- Which columns and rows should be converted;
- The transformation to apply to the data;
- Approve reject any request that may affect the scope, such as changes to detox cleaning activities or new requirements;
General guidelines should be provided on design principles which are of general importance as a whole to facilitate fact-based decision making when design principles may appear to conflict.