Longitudinal Usage Analytics for Government Business Intelligence: A 22-Year Empirical Study of User Adoption, Query Consumption Growth, and Value Realization Patterns

Abstract

Government business intelligence (BI) platforms represent long-lived capital investments whose value accrues over decades rather than fiscal quarters, yet empirical evidence on their long-run adoption and consumption dynamics remains scarce. This study presents a 22-year (2003–2024) longitudinal analysis of a statewide government BI platform serving executive, fiscal, and operational users. We characterize three intertwined phenomena: user adoption, which follows a logistic diffusion trajectory; query consumption growth, which compounds at approximately 27% annually; and value realization, modeled as cumulative decision value net of total cost of ownership. Drawing on de-identified usage telemetry comprising millions of query events, we fit growth models, estimate saturation points, and quantify the lag between adoption and measurable value. Results indicate that value realization accelerates only after adoption crosses an inflection threshold, that query intensity per active user grows even as headcount saturates, and that the platform reached positive cumulative net value in year nine. The findings offer public-sector decision makers a defensible, evidence-based framework for forecasting demand, sizing infrastructure, and justifying continued investment in mission-critical analytics platforms.