Descriptive, Diagnostic, Predictive, and Prescriptive Analytics

Descriptive Analytics:

Descriptive analytics focuses on describing past data to understand what has happened. It involves summarizing and organizing historical data. An example of descriptive analytics is generating reports on website traffic over the past month.

Diagnostic Analytics:

Diagnostic analytics involves analyzing data to determine why an event happened. It focuses on identifying the root cause of a particular outcome. An example of diagnostic analytics is investigating a drop in sales to identify the factors contributing to it.

Predictive Analytics:

Predictive analytics uses statistical algorithms and machine learning techniques to predict future outcomes based on historical data. It helps in forecasting trends and patterns. An example of predictive analytics is using customer data to predict the likelihood of a customer making a purchase in the future.

Prescriptive Analytics:

Prescriptive analytics goes beyond predicting future outcomes by recommending actions to achieve desired results. It takes into account various constraints and scenarios to provide optimal solutions. An example of prescriptive analytics is recommending personalized product recommendations to increase sales based on customer preferences.

By understanding the differences between descriptive, diagnostic, predictive, and prescriptive analytics, organizations can make informed decisions and drive business growth.