A Decision-Driven Methodology for Business Intelligence Dashboards in Start-Ups
Many businesses rely on data-driven metrics to inform critical decisions and drive success. Data reporting is a common way to facilitate these decisions so businesses may effectively achieve their mission. However, ‘data’ can present a significant obstacle for companies if not appropriately presentable for stakeholders while maintaining accuracy and reliability. The work herein describes the design of dashboard systems that effectively display important metrics based on user and enterprise needs for a startup. Through utilizing user design principles and identifying business needs, we applied the following discovery methodology: understanding and familiarizing ourselves with enterprise data, conducting discovery interviews with stakeholders, and distilling interviews into key business decision needs. From here, we determined system objectives for each decision and metrics for each objective. This methodology, which focuses more on the centrality of a dashboard in supporting decisions than many published methodologies, is applied to a specific case, a financial technology startup that focuses on loans. The process involved simultaneously building an understanding of the business, discovering and identifying business decisions, wireframing the data reports, verifying that the data reports effectively and accurately guide the business decisions, and realizing the data reports in a Business Intelligence platform. Applying this methodology to the startup revealed several key takeaways: the importance of pivoting quickly from paper prototypes to prototypes using the final product software, distinctly identified decision traceability for each dashboard element, and iteration between stakeholder engagement, product development, and testing.
Authors
- Reid Bailey (ORCID: https://orcid.org/0000-0003-1685-5225)
- Abimbola Pratt
- Michelle Gallagher
- Claire Novak
- Jenna Tuohy
Institutions
- Engineering Systems (United States) (US)
- University of Virginia (US)
Publication Details
- Published
- 2025-05-02
- DOI
- https://doi.org/10.1109/sieds65500.2025.11021199
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00