Descriptive analytics is a process that involves collecting, processing, and analyzing historical data to provide insights into past events. It allows businesses and organizations to understand what has happened over a specific period by summarizing data into metrics or visual reports. This form of analytics typically uses basic statistical techniques to describe the features of the data set, such as averages, totals, percentages, and trends. The primary goal is to gain a clearer understanding of past performance and operations, which can help inform future decisions.
One common use case for descriptive analytics is in sales performance analysis. For instance, a retail company might analyze sales data from the last year to identify trends in customer purchases. By examining metrics like total sales, best-selling products, and seasonal variations, the company can determine which products are performing well and during which times of the year. Similarly, banks often use descriptive analytics to track customer transactions over time to assess usage patterns and identify services that may need improvement or promotion.
Another application can be found in operations management. Manufacturing companies often utilize descriptive analytics to monitor production output and equipment efficiency. By analyzing historical production data, managers can identify bottlenecks in the supply chain or variations in production quality. This information allows for better resource allocation and can highlight areas needing process improvements. In summary, descriptive analytics serves as a foundation for understanding past performance and informing strategies for future actions in various industries.