Big Data, Analytics, and Data Science Life Cycle - Getting Started with Greenplum for Big Data Analytics - page 19

In addition, this is the phase where you can underscore the business benefits of the
work, and begin making the case to eventually put the logic into a live production
environment.
As a result of this phase, we would have documented the key findings and major
insights as a result of the analysis. The artifact as a result of this phase will be the
most visible portion of the process to the outside stakeholders and sponsors, and
hence should clearly articulate the results, methodology, and business value of the
findings.
Finally, engaging this whole process by implementing it on production data com-
pletes the life cycle. The following steps include the engagement process:
1. Execute a pilot of the previous formulation.
2. Run assessment of the outcome for benefits.
3. Publish the artifacts/insights.
4. Execute the model on production data.
5. Define/apply a sustenance model.