STRATALYCS Technologies starts the data mining project with the understanding of the business problem. Data mining experts, business experts, and domain experts work closely together to define the project objectives and the requirements from a business perspective. The project objective is then translated into a data mining problem definition. In the problem definition phase, data mining tools are not yet required.

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:
1. Identifying your business goals
2. Assessing your situation
3. Defining your data mining goals
4. 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:
1. Gathering data
2. Describing
3. Exploring
4. Verifying quality

Data preparation: Get your data ready to use for modeling. Tasks for this phase include:
1. Selecting data
2. Cleaning data
3. Constructing
4. Integrating
5. Formatting

Modeling: Use mathematical techniques to identify patterns within your data. Tasks for this phase include:
1. Selecting techniques
2. Designing tests
3. Building models
4. Assessing models
Evaluation: Review the patterns you have discovered and assess their potential for business use. Tasks for this phase include:
1. Evaluating results
2. Reviewing the process
3. Determining the next steps

  Deployment: Put your discoveries to work in everyday business. Tasks for this phase include:
1. Planning deployment
2. Reporting final results
3. Reviewing final results