Data Mining Implementation Process
Many industries such as manufacturing, marketing, aerospace, and chemical industries use data mining to improve business performance and decision-making. Because of this wide usage, a standard process was needed to implement data mining effectively.
To solve this, the Cross-Industry Standard Process for Data Mining (CRISP-DM) was introduced in the 1990s with contributions from more than 300 organizations. This framework provides a structured and repeatable way to carry out data mining projects, even for people with limited technical knowledge.
Data mining is the process of discovering useful patterns, relationships, and hidden information from large amounts of data stored in databases or data warehouses. It uses techniques such as Artificial Intelligence (AI), Machine Learning, and Statistics to analyze data and extract valuable insights.
Cross-Industry Standard Process for Data Mining (CRISP-DM)
CRISP-DM consists of six phases, and the process is cyclical, meaning the steps can be repeated until the desired result is achieved.
The six phases are:
- Business Understanding
- Data Understanding
- Data Preparation
- Modeling
- Evaluation
- Deployment
1. Business Understanding
This phase focuses on understanding the business problem and project objectives. The main goal is to convert the business problem into a data mining problem and create a plan to achieve the goal.
Tasks:
Determine Business Objectives
- Understand what the organization wants to achieve.
- Identify the key factors that may affect the project results.
- Clearly define the business goals.
Assess the Situation
- Analyze available resources, project constraints, assumptions, and risks.
Determine Data Mining Goals
- Define technical objectives based on business goals.
Example:
- Business goal: Increase product sales to existing customers.
- Data mining goal: Predict how many products a customer may buy based on age, income, location, and previous purchase data.
- Produce a Project Plan
- Create a roadmap for the project.
- Identify tools, techniques, and steps needed to complete the project.
2. Data Understanding
This phase begins with collecting data and learning about its structure and quality. The aim is to understand the dataset and identify patterns or issues.
Tasks:
Collect Initial Data
- Gather data from available sources such as databases or files.
- Load data into analysis tools if needed.
Describe Data
Examine basic information such as:
- number of records
- number of attributes
- data types
Explore Data
- Use visualization, queries, and statistical analysis to find patterns.
- Identify relationships between variables.
Examples include:
- Distribution of variables
- Summary statistics
- Relationships between attributes
Verify Data Quality
- Check for missing values, errors, inconsistencies, or duplicates in the data.
3. Data Preparation
Data preparation is usually the most time-consuming phase, often taking up to 90% of the total project time. The goal is to convert raw data into a clean and usable dataset for modeling.
Tasks:
Select Data
- Choose relevant datasets and attributes required for analysis.
Clean Data
- Handle missing values.
- Remove incorrect or duplicate data.
Construct Data
- Create new variables or features from existing data.
Example:
- Calculating total purchase value from quantity and price.
Integrate Data
- Combine data from different sources, tables, or systems.
Format Data
- Convert data into the required format for modeling tools.
Example:
- Changing text data into numerical values.
4. Modeling
In this phase, machine learning or statistical models are applied to the prepared data to identify patterns and make predictions.
Tasks:
Select Modeling Technique
Choose suitable algorithms such as:
- Decision Trees
- Neural Networks
- Classification Algorithms
- Regression Models
Generate Test Design
Divide the dataset into:
- Training set (to build the model)
- Testing set (to evaluate the model)
Build Model
- Run the selected algorithms on the prepared data.
Assess Model
- Evaluate how well the model performs.
- Check if the results make sense from a business perspective.
5. Evaluation
After building the model, it is important to verify whether the results actually solve the business problem.
Tasks:
Evaluate Results
Measure how well the model achieves the business objectives.
Review Process
Review all previous steps to ensure nothing important was missed.
Determine Next Steps
Decide whether to:
- Deploy the model
- Improve the model
- Collect additional data
- Start a new data mining project
6. Deployment
In the deployment phase, the final model and insights are implemented in real business operations.
The deployment can be simple or complex depending on the business needs.
Examples include:
- Generating reports
- Integrating models into business systems
- Using predictions for decision-making
Tasks:
Plan Deployment
- Decide how the results will be used in the organization.
Plan Monitoring and Maintenance
- Monitor model performance over time.
- Update the model when business conditions or data change.
Produce Final report
- Document the project results, process, and findings.
Review Project
- Analyze what worked well and what could be improved in future projects.
