1. CRISP Process
Stephen Wyrick

2. Phase One: Business Understanding
Determine the Business Objectives
Understanding the client’s true goals
Assess the Situation
Outline the resources available to accomplish the data mining project
The data analyst should:
List project risks
List potential solutions to those risks
Create a glossary of business and data mining terms
Construct a cost-benefit analysis for the project

3. Phase One: Business Understanding
Determine the Data Mining Goals
State project objectives in business terms
Produce a Project Plan
This plan describes the intention for achieving the data mining goals
This includes:
Outlining specific steps and a proposed timeline
An assessment of potential risks
An initial assessment of the tools and techniques needed to support the project

4. Phase Two: Data Understanding
Collect the Initial Data
Acquire necessary data
Make sure to report problems encountered and the solutions
Describe the Data
Examine the properties of the data
Report on the results
Key question: does the data acquired satisfy the relevant requirements?

5. Phase Two: Data Understanding
Explore the Data
Answer the data mining questions
Create an exploration report
Verify Data Quality
Examine the quality of the data

6. Phase Three: Data Preparation
Select Data
Decide what data will be included and excluded for the analysis
Clean Data
Select clean subsets from the data
Construct Data
After data is cleaned, you should do data preparation operations
This includes developing new records or producing derived attributes
Integrate Data
Combine information from multiple tables or records
Format Data
Change the format or design of the data if needed

7. Phase Four: Modeling Select the Modeling Technique
Choose the specific modeling technique to use
Generate Test Design
Separate data into a test and train set
Build the model on the train set
Estimate its quality on the separate test set
Build the Model
Create one or more models by running the modeling tool
Assess the Model
Interpret the models
Rank the models

8. Phase Five: Evaluation
Evaluate Results
Assess the degree to which the model meets the business objectives
Determine if there is some business reason why this model is deficient
Summarize the assessment results
Review Process
A more thorough review of the data mining engagement
Covers quality assurance
Determine Next Steps
Must decide whether to finish this project and move on to deployment or whether to initiate further iterations

9. Phase Six: Deployment Plan Deployment Plan Monitoring and Maintenance
Takes evaluation results and develops a strategy for deployment
Plan Monitoring and Maintenance
A carefully prepared maintenance strategy avoids incorrect usage of results
Produce Final Report
Includes previous deliverables and summarizes and organizes the results
Review Project
Assess failures and successes to help improve future projects