Evaluating the ROI of Self-Service Business Intelligence Tools Across Functional Business Units

Self-Service Business Intelligence (SSBI) tools have revolutionized corporate decision
making by decentralizing analytics capabilities from central IT departments to end-user
functional business units. However, quantifying the financial Return on Investment (ROI)
and operational efficacy of SSBI platforms across heterogeneous organizational functions
remains a major challenge due to unobserved productivity gains, dynamic software licensing
structures, and data governance overheads. This paper presents a novel multi-dimensional
non-linear evaluation model engineered to capture both financial and non-financial metrics
associated with SSBI adoption. Utilizing an empirical enterprise dataset tracking 8,500
active SSBI users across Finance, Marketing, Operations, and Sales across 45 enterprise
organizations between 2018 and 2023, our non-linear evaluation framework demonstrates
that SSBI investments yield an aggregate ROI of 184.2% over a 36-month horizon, reducing
average Time-to-Insight (TTI) by 74.5%. Grounded in non-linear predictive modeling
methodologies and strategic enterprise governance principles, we establish that ROI scaling
follows a non-linear trajectory governed by user autonomy and governance maturity. The
paper incorporates an exhaustive quantitative synthesis incorporating 10 data tables, 10
mathematical behavior graphs, and 20 visually unique chart architectures mapping
organizational analytics performance.