Deployment – Step 6 of the CRISP-Data Mining (DM) Process2022-03-24T11:15:55+00:00
In the last post we explained about evaluation phase of CRISP-DM, now we can discuss deployment phase of the crisp dm process, in this phase, you will come to know about the tactics to deploy results of your evaluation. In case you have identified a general procedure to develop the relevant models, we document this procedure here for later implementation. It is necessary to consider the means of implementation during the business understanding phase too, as deployment is important for the success of the project. In this case, predictive analysis helps in enhancing the operational process of your business.
Deployment Phase ( Step 6)
Deployment plan: Here, your deployment strategy and the steps involved in the process are summarized, besides focussing on the way they are to be implemented.
Monitoring the plan and its maintenance
When the results of data mining become a part of the daily business, it is necessary to monitor and keep a track on them. When the maintenance strategy is carefully prepared, it helps in avoiding the wrong implementation of the data mining results over long periods of time. Evidently, a detailed plan for monitoring the process has to be developed for monitoring the implementation of the data mining results. This data monitoring plan includes the specific type of implementation.
Plan for monitoring and maintenance
This process involves summarizing and monitoring the maintenance policy. This includes the steps included in the process, as well as the method to perform them.
Production of the final report
You need to write a final report at the end of the project. This report, according to the deployment plan, may only contain the project summary, provided they were not already documented. It may also be presented as a comprehensive final presentation of the results of your data mining project.
Final report: This report contains the final verdict of the engagement of data mining. It is inclusive of all the deliverables used previously, organizing and summarizing the results.
Final presentation: At the end of the project, you need to arrange for a meeting, where you need to present the results to the customer.
Reviewing the project
While reviewing the project, you need to assess whether everything went right or not. Identify the loopholes and focus on the areas that you need to improve in.
Experience documentation: Here, it is necessary to summarize the important experience that you have gathered from the project. These include the difficulties you have come across, wrong approaches and hints for choosing the best techniques for data mining in similar situations. The documentation part in ideal projects also includes the reports, that the individual project members had written during the earlier phases of the project.
In the last phase of the data mining project, you need to come up with a powerful data maintenance plan, which will enhance the quality of data for the subsequent projects. In case you need any assistance in data mining, you can reach out to us. At PGBS, you will find a comprehensive support for data mining. We have been delivering these services to our clients over the years. The experts working with us are capable of addressing the data analytics projects in complex scenarios, with high possibilities of success, integrating the CRISP-DM methodology. Feel free to contact us for any assistance with your data mining projects.