Database Reference
In-Depth Information
2.7 Phase 6: Operationalize
In the final phase, the team communicates the benefits of the project more broadly
and sets up a pilot project to deploy the work in a controlled way before broadening
the work to a full enterprise or ecosystem of users. In Phase 4, the team scored
the model in the analytics sandbox. Phase 6, shown in
Figure 2.8
,
represents the
first time that most analytics teams approach deploying the new analytical methods
or models in a production environment. Rather than deploying these models
immediately on a wide-scale basis, the risk can be managed more effectively and the
team can learn by undertaking a small scope, pilot deployment before a wide-scale
rollout. This approach enables the team to learn about the performance and related
constraints of the model in a production environment on a small scale and make
adjustments before a full deployment. During the pilot project, the team may need
to consider executing the algorithm in the database rather than with in-memory
tools such as R because the run time is significantly faster and more efficient than
running in-memory, especially on larger datasets.
