This task takes the evaluation results and determines a strategy for deployment. If a general procedure has been identified to create the relevant model(s), this procedure is documented here for later deployment. Our practical experience tells us that it makes sense to consider the ways and means of deployment during the business understanding phase as deployment is crucial to the success of the project. This is where predictive analytics really helps to improve the operation side of your business.
- Deployment plan – summarise the deployment strategy including the necessary steps and how to perform them.
Plan monitoring and maintenance
Monitoring and maintenance are important issues if the data mining result becomes part of the day-to-day business and its environment. The careful preparation of a maintenance strategy helps to avoid unnecessarily long periods of incorrect usage of data mining results. In order to monitor the deployment of the data mining result(s), the project needs a detailed monitoring process plan. This plan takes into account the specific type of deployment.
- Monitoring and maintenance plan – summarise the monitoring and maintenance strategy, including the necessary steps and how to perform them.
Produce final report
At the end of the project, the project team writes up a final report. Depending on the deployment plan, this report may be only a summary of the project and its experiences (if they have not already been documented as an ongoing activity) or it may be a final and comprehensive presentation of the data mining result(s).
- Final report – this is the final written report of the data mining engagement. It includes all of the previous deliverables, summarising and organising the results.
- Final presentation – there will also often be a meeting at the conclusion of the project at which the results are presented to the customer.
Assess what went right and what went wrong, what was done well and what needs to be improved.
- Experience documentation – summarise important experience gained during the project. For example, pitfalls, misleading approaches, or hints for selecting the best suited data mining techniques in similar situations could be part of this documentation. In ideal projects, experience documentation also covers any reports that have been written by individual project members during previous phases of the project.