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Summary
The research investigates the development process of BI dashboards which transform into decision-support tools instead of using a fixed design approach. The multi-stage Power BI case study which uses fictional retail data demonstrates how feedback and gap analysis and executive questions improve both analytical accuracy and decision-making effectiveness.
Contribution
The main contribution is a replicable, feedback-driven dashboard refinement methodology. The research creates a narrative framework which demonstrates how technical and interpretive Business Intelligence practice enhancements develop together through DAX-based modifications.
Relevance
The research results deliver value to BI practitioners and educators because they address the common issue which exists between dashboard appearance and decision-making functionality. The system operates through an iterative process because this methodology aligns with actual business practices for BI work implementation.
Approach
The paper needs to use a case-based iterative method because this approach enables it to achieve its practice-focused objectives. The analytical process follows typical Business Intelligence scenarios although the dataset and organizational structure exist only as fictional elements.
Strengths
The system shows how business intelligence design requires multiple stages of development which follow each other in real-world business applications.
The executive questions create direct connections which analysts can use to improve their analytical efforts.
Practical insights into narrative coherence and dashboard usability
Limitations
The research findings stem from one made-up case study which prevents researchers from drawing universal conclusions. The research would achieve greater impact through validation which should occur in actual business environments.
Overall assessment
The research establishes a method to monitor dashboard development into decision intelligence tools through controlled improvement stages which enhance actual Business Intelligence systems.
The author declares that they have no competing interests.
The author declares that they did not use generative AI to come up with new ideas for their review.
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