Business Intelligence for U.S. Agricultural Trade: Design and Implementation of an Interactive Power BI Dashboard
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Authors
Ghaed, Maryam
Issue Date
2025
Type
Thesis
Language
en_US
Keywords
Alternative Title
Abstract
Reliable agricultural trade information is essential for guiding production, policy,and market decisions, yet these datasets are often scattered, inconsistently formatted,
and difficult for non-specialists to interpret. This thesis presents an interactive vi-
sualization system that integrates multiple U.S. Department of Agriculture (USDA)
datasets including the Foreign Agricultural Trade of the United States (FATUS) and
state-level trade summaries into a unified analytical dashboard built in Microsoft
Power BI.
The dashboard allows users to explore trade patterns across products, states, and
years using interactive maps, bar charts, and key performance indicators. Distinct
color schemes clearly separate exports and imports, helping users follow trade flows
and compare major commodities such as corn, soybeans, and cotton. Filters support
flexible analysis, making it possible to observe how trade relationships shift over time
and across regions.
Validation included technical checks and a usability study with graduate students
and researchers at the University of Nevada, Reno. Participants completed a set of
analytical tasks and rated the dashboard’s navigation, filters, and visual clarity. Most
tasks were answered accurately, with only minor confusion on questions involving
geographic interpretation. Questionnaire ratings were consistently positive. ANOVA
results showed no significant effects of computer proficiency or frequency of data-tool
use, but familiarity with visualization tools did have a significant impact, with more
experienced users reporting smoother navigation and clearer filter interactions.
Overall, the dashboard effectively transforms fragmented agricultural trade data
into clear, interactive insights. It supports accessible exploration of U.S. trade pat-
terns for users with diverse experience levels and offers a practical, scalable model for
developing analytical dashboards in other domains requiring transparency, usability,
and data-driven understanding.