The Cross-Industry Standard Process for Data Mining (CRISP-DM) is the dominant data-mining process framework. It's an open standard; anyone may use it. The following list describes the various phases of the process.
Business understanding: Get a clear understanding of the problem you're out to solve, how it impacts your organization, and your goals for addressing it. Tasks in this phase include:
Identifying your business goals
Assessing your situation
Defining your data mining goals
Producing your project plan
Data understanding: Review the data that you have, document it, identify data management and data quality issues. Tasks for this phase include:
Gathering data
Describing
Exploring
Verifying quality
Data preparation: Get your data ready to use for modeling. Tasks for this phase include:
Selecting data
Cleaning data
Constructing
Integrating
Formatting
Modeling: Use mathematical techniques to identify patterns within your data. Tasks for this phase include:
Selecting techniques
Designing tests
Building models
Assessing models
Evaluation: Review the patterns you have discovered and assess their potential for business use. Tasks for this phase include:
Evaluating results
Reviewing the process
Determining the next steps
Deployment: Put your discoveries to work in everyday business. Tasks for this phase include:
Planning deployment (your methods for integrating data mining discoveries into use)
Reporting final results
Reviewing final results