Enterprise analytics initiatives frequently produce models, dashboards, and operational signals without consistently translating them into measurable business value. Finance functions are structurally positioned to close this gap because they can connect operational signals to cash, margin, working capital, revenue risk, cost avoidance, and capital efficiency. This paper proposes a Finance-Led Analytics Value Realization Framework for structuring analytics initiatives so that operational signals translate into governed financial outcomes. The framework consists of six stages: business problem framing, financial value hypothesis, cross-system signal mapping, action ownership, governance and controls, and benefits realization tracking. The proposed architecture preserves enterprise systems of record while adding a finance-led analytics control layer for Physical Data Element (PDE) mapping, signal extraction, value-driver translation, rule orchestration, workflow activation, and outcome measurement. A discrete-event simulation was conducted over 180 operating days using 12 fragmented enterprise systems, 64 operational signal classes, 420 decision owners, 1.2 million transaction records, and five baseline operating models: technology-led dashboarding, analyst-led reporting, centralized data science queueing, process-mining diagnosis, and program-management-office benefits tracking. Results show that the proposed methodology increased analytically attributed value capture from 31.8% to 74.6%, reduced median signal-to-value latency from 61.2 h to 15.8 h, improved benefits traceability from 44.5% to 91.3%, reduced manual value-reconciliation effort by 62.4%, and generated USD 8.36 million in annualized net economic value. The findings position finance-led operational analytics as a value-governance discipline rather than a reporting or modeling activity.
Thatikonda et al. (Fri,) studied this question.