Multi-Platform Business Intelligence (BI) Lifecycle Management: Data-Driven Framework for Report Usage Analysis and Visualization

As business intelligence (BI) platforms grow increasingly complex, organizations struggle to manage redundant, stale, and low-value reports across systems like Power BI, SQL Server Reporting Services (SSRS), Qlik, and Maestro (formerly RapidResponse). This project developed an automated data analytics framework and dashboard to enable comprehensive report usage auditing, trend analysis, and lifecycle management.

Report usage data was aggregated from multiple BI platforms and standardized to support consistent cross-platform analysis. The data model captured essential metadata—report name, platform, user ID, access date, and view counts—enabling calculation of key performance indicators including unique users per report, viewing trends over time, and recency metrics. A Power BI dashboard was built using DAX (Data Analysis Expressions) to deliver interactive visualizations and filters, empowering the reporting team to monitor usage patterns, identify high- and low-engagement reports, and assess data freshness.

The resulting tool enables data-driven decisions around training needs, technical enhancements, and retirement of underutilized reports. Reports are automatically classified as "active," "stale," or "at risk," while stakeholders can explore visual summaries, time-series trends, and drill-downs by platform and report owner. A complementary process map defines workflows triggered by stale report classification—including review, validation, and archiving procedures—ensuring analytical insights translate directly into actionable governance.

This project demonstrates the value of cross-platform analytics and dynamic visualization in optimizing digital resource management. The methodology is repeatable, platform-agnostic, and scalable for large data ecosystems, advancing data governance while supporting sustainable digital operations by reducing BI sprawl and enhancing report quality across business functions.

Dr. Greene is a faculty member in the School of Systems Science and Industrial Engineering within the Watson College of Engineering and Applied Science at Binghamton University. Prior to joining Binghamton, he served as a manufacturing scientist specializing in quality engineering in IBM's Microelectronics Division and held faculty positions at the University of Alabama and the Rochester Institute of Technology. His primary research focuses on Industry 4.0/5.0, particularly the continuous improvement of manufacturing and service industries through engineering science, collaborative robotics (cobots), simulation, and cloud-based cobotics. He has applied these improvement methodologies to electronics manufacturing and healthcare systems.

Ms. Loo is an undergraduate in her senior year majoring in Industrial and Systems Engineering.