Descriptive Analytics: Descriptive analytics deals with summarizing data to understand what has happened in the past. This type of analytics provides insights into historical performance and trends. It answers questions like "What happened?" and "When did it happen?"
Diagnostic Analytics: Diagnostic analytics focuses on analyzing past data to understand why something happened. This type of analytics helps to identify the root cause of a problem or anomaly. It answers questions like "Why did it happen?" and "What were the contributing factors?"
Predictive Analytics: Predictive analytics uses statistical models and machine learning algorithms to analyze historical data and make predictions about future outcomes. This type of analytics helps businesses to anticipate future trends and make data-driven decisions. It answers questions like "What is likely to happen?" and "What are the chances of a certain event occurring?"
Prescriptive Analytics: Prescriptive analytics goes beyond prediction and recommends actions to take based on the predictions made. This type of analytics helps businesses to make informed decisions by suggesting the best course of action to achieve a specific goal. It answers questions like "What should we do to achieve a certain outcome?" and "What actions will lead to the desired result?"
Cognitive Analytics: Cognitive analytics involves the use of artificial intelligence and natural language processing techniques to analyze unstructured data such as text and images. This type of analytics helps businesses to extract valuable insights from large amounts of unstructured data that would be difficult to analyze using traditional methods. It answers questions like "What do customers think about our product?" and "What is the sentiment of social media posts about our brand?"
