Objective: Provides insights into past events by summarizing raw data into actionable information.
Examples: Reports, dashboards, and data visualization that explain key metrics like sales figures, production rates, or customer behavior.
2. Diagnostic Analytics (Why Did It Happen?)
Objective: Examines historical data to determine the causes behind certain events or trends.
Examples: Root cause analysis and correlation analysis used to understand why sales dropped or why a machine failed.
3. Predictive Analytics (What Will Happen?)
Objective: Uses historical data and statistical models to forecast future outcomes or trends.
Examples: Demand forecasting, risk assessment, and predictive maintenance where AI models predict equipment failures.
4. Prescriptive Analytics (How Can We Make It Happen?)
Objective: Recommends actions based on predictive insights to achieve desired outcomes.
Examples: Optimization algorithms, recommendation systems, and automated decision-making in supply chain planning or financial management.
Summary of the Continuum:
The framework starts from understanding what happened (descriptive), moves to why it happened (diagnostic), progresses to what will happen (predictive), and finally focuses on how to make it happen (prescriptive).
Advanced analytics blends these approaches with sophisticated algorithms, enabling better decision-making and automation, especially relevant for large-scale IoT data.
