Analytics Process Model & Professional Roles

1. The Analytics Process Model

The analytics process is a structured, iterative approach to solving problems using data.

Study Deep: The "Cleaning" Bottleneck

In professional analytics, the most important phase is often the most tedious: Data Pre-processing.

• The 80/20 Rule: Data professionals typically spend 80% of their time cleaning and organizing data, and only 20% performing actual analysis or modeling.
• Why?: Real-world data is "dirty"—it has missing values, inconsistent formats, and human entry errors. A model trained on bad data produces bad results (GIGO - Garbage In, Garbage Out).

1. The Analytics Process Model

The analytics process is a structured, iterative approach to solving problems using data. While different frameworks exist (like CRISP-DM), most follow these 7 core stages:

2. Professional Roles in Analytics

The analytics pipeline requires different skills at different stages.

3. The Analytics Lifecycle (CRISP-DM)

Cross-Industry Standard Process for Data Mining (CRISP-DM) is the most widely used industry framework.

• Key Feature: It is Non-Linear. For example, if evaluation (Stage 6) shows poor results, you might go back to Data Pre-processing (Stage 3) to improve the data quality.