Analytics:

• Descriptive Analytics: “What has happened?”

Descriptive analytics provides insights into past events, trends, and patterns. Examples include generating reports, visualizing data, and creating dashboards.

Diagnostic Analytics: A subset of descriptive analytics, diagnostic analytics aims to explain why something happened.

It investigates the causes behind specific events or anomalies. For instance, if sales declined last quarter, diagnostic analytics would explore factors like marketing campaigns, economic conditions, or product changes to identify the root cause.

• Predictive Analytics: Predictive analytics uses statistical models and machine learning algorithms to forecast future outcomes. It answers questions like “What will happen?”

By analyzing historical data, predictive models make predictions about future events. Examples include predicting stock prices, customer churn, or weather conditions.

• Prescriptive Analytics: This type goes beyond prediction and recommends actions to optimize outcomes. It answers the question “What should happen?”

Prescriptive analytics suggests specific strategies or decisions based on data. For instance, it might recommend adjusting inventory levels, optimizing supply chains, or personalizing marketing offers.