Beyond Visualization: Building Decision Intelligence Through Iterative Dashboard Refinement
Likitha Tadakala, Muskan Saraf, Sajjad Rezvani Boroujeni, Hossein Abedi, Tom Bush

TL;DR
This paper demonstrates how iterative dashboard refinement, guided by structured feedback and gap analysis, enhances decision support in business intelligence, illustrated through a case study on profitability analysis in retail.
Contribution
It introduces a replicable iterative methodology with technical and narrative frameworks, advancing BI dashboard design practices.
Findings
Margin erosion identified in furniture segment
Profitability declines after 20% discount threshold
Unrecovered shipping costs amount to $1.35 million
Abstract
Effective business intelligence (BI) dashboards evolve through iterative refinement rather than single-pass design. Addressing the lack of structured improvement frameworks in BI practice, this study documents the four-stage evolution of a Power BI dashboard analyzing profitability decline in a fictional retail firm, Global Superstore. Using a dataset of $12.64 million in sales across seven markets and three product categories, the project demonstrates how feedback-driven iteration and gap analysis convert exploratory visuals into decision-support tools. Guided by four executive questions on profitability, market prioritization, discount effects, and shipping costs, each iteration resolved analytical or interpretive shortcomings identified through collaborative review. Key findings include margin erosion in furniture (6.94% vs. 13.99% for technology), a 20% discount threshold beyond…
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Taxonomy
TopicsBig Data and Business Intelligence · Data Visualization and Analytics · Spreadsheets and End-User Computing
