What is the difference between descriptive, predictive, and prescriptive analytics?

Descriptive, predictive, and prescriptive analytics are distinct approaches used in data analysis to help businesses make informed decisions. Each serves a unique purpose and provides different types of insights:

1. Descriptive Analytics:

• Purpose: Describes historical data to understand past behaviors or outcomes.
  
• What it Does: It answers the "What happened?" question by summarizing data through reports, dashboards, and visualizations.
  
• Example: Analyzing sales data from the previous quarter to identify trends or patterns.
  
• Tools Used: Dashboards, reports, and business intelligence tools.
  

2. Predictive Analytics:

• Purpose: Forecasts future outcomes based on historical data and patterns.
  
• What it Does: It answers the "What is likely to happen?" question by using statistical models and machine learning techniques to predict future trends or behaviors.
  
• Example: Using historical sales data to predict future sales, customer behavior, or demand for products.
  
• Tools Used: Regression analysis, machine learning models, and forecasting tools.
  

3. Prescriptive Analytics:

• Purpose: Recommends actions to optimize decisions and outcomes.
  
• What it Does: It answers the "What should we do?" question by analyzing data and suggesting the best course of action to achieve desired outcomes.
  
• Example: Recommending inventory levels to maintain based on predictive sales and optimizing marketing spend to maximize ROI.
  
• Tools Used: Optimization models, simulations, and decision analysis tools.
  

Summary:

Descriptive analytics provides insights into the past, predictive analytics forecasts future events, and prescriptive analytics recommends optimal actions to achieve goals.

Together, these three types of analytics help organizations understand, forecast, and take proactive steps to improve performance and decision-making.