Phases of the Data Mining Process

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