Implementation of Business Intelligence with Power BI in Agricultural Commodities Traded in the United States

Resumen

Business intelligence makes it possible to efficiently visualize large volumes of data, thereby supporting a wide range of decision makers. In this study, a business intelligence tool was implemented to analyze the prices and quantities of agricultural commodities traded in the United States using Power BI. The objective was to design an algorithm that, through the My Market News Application Programming Interface (API), downloads three types of reports—terminal market, shipping point, and movement—bringing in information from 2015 onward. An extract, transform and load (ETL) process was established to automatically cleanse and unify the data, enabling seamless updates. Finally, a relational model was built that facilitated the creation of user-friendly interactive dashboards (control panels). The procedure included Python scripts to streamline the downloading, cleansing, and refreshing of the information. In addition, Power Query and DAX were employed to enhance data cleaning and ensure accurate visualization. The end product was a report containing five dashboards: “Prices and Quantity,” “Conventional vs. Organic,” “Seasonal Analysis,” “Quantity Analysis,” and “Origin Analysis”. Moreover, near real time updates allow users to monitor market trends and support strategic decisions. The application of these visualizations enables producers, buyers, sellers, and analysts to identify trends and seasonality in key commodity variables—such as price, quantity, variety, and origin—thereby greatly facilitating informed decision making.