At the forefront of every sport, league, and team across the globe lies descriptive analytics. It is the process of sorting through historical data as a means of understanding current changes and shifts that have taken place. Looking at these trends, analysts are able to understand previous and potentially future business trends to maximize profits and reach their goals. Descriptive analytics highlights both strengths and weaknesses within a business and provides a jump off point for altering company strategies. Additionally, descriptive analytics when paired with other analytics such as predictive and prescriptive allows for greater data synthesis and more in-depth business strategy development.
Descriptive analytics is performed on data collected through two different strategies. The first is data aggregation. Data aggregation is the compilation of data from various different large scale bases and sources. The second collection strategy is data mining. In data mining a computer searches for patterns in large sets of data and pulls specific pieces of it. With these two methods, descriptive analytics pulls out any trends that it finds for analysts to then read and strategize over.
Data extraction, trend analysis, and data visualization are some of the many things that descriptive analytics does. It is the visual and numerical form of the question What happened? within the business world. Because of this form of analytics, companies are able to make more informed decisions for the future by looking back into their past. It is an integral way to avoid
repeating mistakes and to leverage strategies that will work best for all parties involved.