Which methods are commonly used in descriptive analytics?

Descriptive analytics focuses on summarizing historical data to understand what has happened in the past. This is achieved through various methods that help organizations to make sense of their data.

Data mining is a technique used to discover patterns and insights from large datasets. It allows analysts to explore data and identify trends that may not be immediately apparent. Data aggregation involves compiling data from different sources into a coherent summary, making it easier to analyze overall trends. Lastly, data visualization plays a crucial role by representing data graphically, which helps stakeholders quickly grasp complex information and identify patterns or anomalies.

These methods combined provide a comprehensive approach to understanding past performance, allowing businesses to gain insights that inform future decisions. In contrast, the other options involve techniques that are more predictive or prescriptive in nature rather than descriptive, focusing on forecasting and modeling future scenarios rather than summarizing past data.