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| Data Migrations Series |
Repeatability
Repeatability is the degree with which a DM can be repeated and obtain consistent results between repetitions. Even if a DM is supposed to be a one-time activity for a project, to guarantee a certain level of quality it’s important to consider several iterations in which the data requirements are refined and made sure that the data can be imported as needed into the target system(s). Considered as a process, as long the data and the rules haven’t changed, the results should be the same or have the expected level of deviation from expectations.
This requirement is important especially for the data migrated during UAT and Go-Live, time during which the input data and rules need to remain frozen (even if small changes in the data can still occur). In fact, that’s the role of UAT – to assure that the data have the expected quality and when compared to the previous dry-run, that it attains the expected level of consistency.
Reusability
Reusability is the degree to which the whole solution, parts of the logic or data can be reused for multiple purposes. Master data and the logic associated with them have high reusability potential as they tend to be referenced by multiple entities.
Modularity
Modularity is the degree to which a solution is composed of discrete components such that a change to one component has minimal impact on other components. It applies to the solution itself but also to the degree to which the logic for the various entities is partitioned so to assure a minimal impact.
Partitionability
Partitionability is the degree to which data or logic can be partitioned to address the various requirements. Despite the assurance that the data will be migrated only once, in practice this assumption can be easily invalidated. It’s enough to increase the system freeze by a few days and/or to have transaction data that suddenly requires master data not considered. Even if the deltas can be migrated in system manually, it’s probably recommended to migrate them using the same logic. Moreover, the performing of incremental loads can be a project requirement.
Data might need to be partitioned into batches to improve processing’s performance. Partitioning the logic based on certain parameters (e.g. business unit, categorical values) allows more flexibility in handling other requirements (e.g. reversibility, performance, testability, reusability).
Performance
Performance refers to the degree a piece of software can process data into an amount of time considered as acceptable for the business. It can vary with the architecture and methods used, respectively data volume, veracity, variance, variability, or quality.
Performance is a critical requirement for a DM, especially when considering the amount of time spent on executing the logic during development, tests and troubleshooting, as well for other activities. Performance is important during dry-runs but more important during Go-Live, as it equates with a period during which the system(s) are not available for the users. Upon case, a few hours of delays can have an important impact on the business. In extremis, the delays can sum up to days.
Predictability
Predictability is the degree to which the results and behavior of a solution, respectively the processes involve are predictable based on the design, implementation or other factors considered (e.g. best practices, methodology used, experience, procedures and processes). Highly predictable solutions are desirable, though reaching the required level of performance and quality can be challenging.
The results from the dry-runs can offer an indication on whether the data migrated during UAT and Go-Live provide a certain level of assurance that the DM will be a success. Otherwise, an additional dry-run should be planned during UAT, if the schedule allows it.
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