Phase 6 of the CRISP-DM Process Model: Deployment
Deployment is where data mining pays off. In this final phase of the Cross-Industry Standard Process for Data Mining (CRISP-DM) process, it doesn’t matter how brilliant your discoveries may be, or how perfectly your models fit the data, if you don’t actually use those things to improve the way that you do business.
The deployment phase includes four tasks. These are
Planning deployment (your methods for integrating data-mining discoveries into use)
Planning monitoring and maintenance
Reporting final results
Reviewing final results
Task: Planning deployment
When your model is ready to use, you will need a strategy for putting it to work in your business.
The deliverable for this task is the deployment plan. This is a summary of your strategy for deployment, the steps required, and the instructions for carrying out those steps.
Task: Planning monitoring and maintenance
Data-mining work is a cycle, so expect to stay actively involved with your models as they are integrated into everyday use.
The deliverable for this task is the monitoring and maintenance plan. This is a summary of your strategy for ongoing review of the model’s performance. You’ll need to ensure that it is being used properly on an ongoing basis, and that any decline in model performance will be detected.
Task: Reporting final results
Deliverables for this task include two items:
Final report: The final report summarizes the entire project by assembling all the reports created up to this point, and adding an overview summarizing the entire project and its results.
Final presentation: A summary of the final report is presented in a meeting with management. This is also an opportunity to address any open questions.
Task: Review project
Finally, the data-mining team meets to discuss what worked and what didn’t, what would be good to do again, and what should be avoided!
This step, too, has a deliverable, although it is only for the use of the data-mining team, not the manager (or client). It’s the experience documentation report.
This is where you should outline any work methods that worked particularly well, so that they are documented to use again in the future, and any improvements that might be made to your process. It’s also the place to document problems and bad experiences, with your recommendations for avoiding similar problems in the future.
Data mining is a team activity. So if this process seems to include a lot of steps, realize that it may not be your personal responsibility to do every one of them, and that it’s always appropriate to ask for help from others when you need it. (At the start of the project, you made a list of people who are resources for the data-mining project. That’s your little directory of helpers!)