The most recognized method for implementing predictive analytics projects is the cross-industry standard Process for Data Mining, also known as CRISP-DM. The sap system process consists of six main stages, as shown below:
Sap system Business Understanding: In the first phase, business needs and business success criteria will be identified and assessed. Translate business requirements into predictive analytics problems and develop goals and success criteria accordingly. A project plan will be developed.
Sap system Data understanding: In the second phase, the available data sources will be reviewed and the data will be analyzed in detail to understand what it corresponds to and whether it is available in the ongoing project.
Data preparation: The third phase focuses on finding, cleaning, and formatting/integrating data as needed.
Predictive modeling: The fourth stage involves selecting appropriate modeling techniques (e.g., classification, regression, or time series forecasting) and generating predictive scenarios and predictive models.
Predictive model Evaluation: In the fifth stage, the predictive model is evaluated by analyzing the training results and reviewing the entire process.
Providing predictions: In Phase 6, trained predictive models can be used effectively by providing predictions to sap system Analytics Cloud users and possibly to other systems through data export.
In the following sections, we walk you through the step-by-step process and detailed tasks required for each of the six project phases.
The first phase of the SAP system predictive analysis project includes determining business expectations and associated impacts; That is, what is the business owner trying to accomplish? You need to spend the time you need to delve into the business goals and what the business considers the success criteria for the project. This information will form the cornerstone of your project plan.
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