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| Data Migrations Series |
Reliability
Reliability is the degree to which a solution performs its intended functions under stated conditions without failure. In other words, a DM is reliable if it performs what was intended by design. The data should be migrated only when migration’s reliability was confirmed by the users as part of the sign-off process. The dry-runs as well the final iteration for the UAT have the objective of confirming solution’s reliability.
Reversibility
Reversibility is the degree to which a solution can return to a previous state without starting the process from the beginning. For example, it should be possible to reverse the changes made to a table by returning to the previous state. This can involve having a copy of the data stored respectively deleting and reloading the data when necessary.
Considering that the sequence in which the various activities is fix, in theory it’s possible to address reversibility by design, e.g. by allowing to repeat individual steps or by creating rollback points. Rollback points are especially important when loading the data into the target system.
Robustness
Robustness is the degree to which the solution can accommodate invalid input or environmental conditions that might affect data’s processing or other requirements (e,g. performance). If the logic can be stabilized over the various iterations, the variance in data quality can have an important impact on a solutions robustness. One can accommodate erroneous input by relaxing schema’s rules and adding further quality checks.
Security
Security is the degree to which the DM solution protects the data so that only authorized people have access to the respective data to the defined level of authorization as data are moved through the solution. The security provided by a solution needs to be considered against the standards and further requirements defined within the organization. In case no such standards are available, one can in theory consider the industry best practices.
Scalability
Scalability is the degree to which the solution is able to respond to an increased workload. Given that the number of data considered during the various iterations vary in volume, a solution’s scalability needs to be considered in respect to the volume of data to be migrated.
Standardization
Standardization is the degree to which technical standards were implemented for a solution to guarantee certain level of performance or other aspects considered as import. There can be standards for data storage, processing, access, transportation, or other aspects associated with the migration processes. Moreover, especially when multiple DMs are in scope, organizations can define a set of standards and guidelines that should be further considered.
Testability
Testability is the degree to which a solution can be tested in the respect to the set of functional and data-related requirements. Even if for the success of a migration are important the data in their final form, to achieve that is needed to validate the logic and test thoroughly the transformations performed on the data. As the data go trough the data pipelines, they need to be tested in the critical points – points where the data suffer important transformations. Moreover, one can consider record counters for the records processed in each such critical point, to assure that no record was lost in the process.
Traceability
Traceability is the degree to which the changes performed on the data can be traced from the target to the source systems as record, respectively at entity level. In theory, it’s enough to document the changes at attribute level, though upon case it might needed to document also the changes performed on individual values.
Mappings at attribute level allow tracing the data flow, while mappings at value level allow tracing the changes occurrent within values.
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