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6. Types of Data Analytics Model

Data analysis can be broadly categorized into three types: descriptive, predictive, and prescriptive. Each of these serves a unique purpose and uses different techniques and methodologies. Here’s a detailed explanation of each type, their applications, and how they differ from each other:

1. Descriptive Analysis

  • Purpose: To summarize past data and describe what has happened.
  • Methodology: Utilizes data aggregation and data mining techniques to provide insight into the past and identify trends based on historical data.
  • Applications:
    • Business Intelligence: Analyzing sales or performance data to understand business trends.
    • Customer Analysis: Identifying the most common or recurring customer behaviors or characteristics.
  • Tools: Commonly uses basic business intelligence tools, dashboards, and visual data exploration tools to summarize and visualize findings.

2. Predictive Analysis

  • Purpose: To predict future outcomes based on historical data.
  • Methodology: Uses statistical models and machine learning algorithms to forecast future events by detecting patterns and relationships in historical data.
  • Applications:
    • Forecasting Demand: Predicting future product demand based on past sales data.
    • Risk Assessment: Estimating the likelihood of future loan defaults in the banking sector.
  • Tools: Involves advanced analytics tools like regression analysis, time series analysis, and various predictive modeling software.

3. Prescriptive Analysis

  • Purpose: To advise on possible outcomes and actions to achieve specific goals.
  • Methodology: Combines insights from predictive analytics with optimization and simulation algorithms to advise on possible outcomes and decision making.
  • Applications:
    • Supply Chain Optimization: Suggesting production adjustments based on predicted demand and resource constraints.
    • Personalized Marketing: Recommending marketing strategies for individual customer segments to maximize engagement and conversion.
  • Tools: Utilizes complex algorithms, simulation models, and decision analysis to provide actionable insights.