A Decision-Driven Methodology for Business Intelligence Dashboards in Start-Ups; The Latent and Manifest Affects of Data Centers

This research bridges the technical and sociotechnical dimensions of data systems by exploring both the design of Business Intelligence (BI) dashboards for startups and the broader societal impact of data storage infrastructure. On the technical side, the work focuses on an iterative methodology for dashboard design that prioritizes decision traceability—linking every data point to a clear system objective. Applied to a fintech startup focused on lending, this approach includes stakeholder interviews, business need identification, wireframing, and implementation using a BI platform. Key insights include the value of early prototyping in production software, aligning metrics to decisions, and frequent iteration between stakeholder input and development. Concurrently, the STS (Science, Technology, and Society) portion investigates the underexamined social and environmental consequences of data centers, which store the majority of the world’s data. While data centers support global connectivity and regional economic growth, they also produce latent dysfunctions—such as energy overconsumption and community disruption—that are rarely addressed. By applying sociological frameworks of manifest and latent functions, this research underscores the urgent need to reimagine data infrastructure in ways that prioritize environmental sustainability and social equity. Together, these two strands emphasize that digital decision-making tools and the physical infrastructures enabling them must be designed with holistic awareness. The future of data systems depends not only on technical efficiency but also on ethical responsibility and long-term adaptability to human and ecological needs.