Data-Driven Performance Management: Evaluating the Role of Predictive Business Intelligence

Abstract

Performance management systems have historically been descriptive: dashboards report key performance indicators (KPIs) after the period closes, and managers react to shortfalls that have already occurred. Predictive business intelligence (BI) promises to shift this cycle forward by forecasting KPIs and flagging units at risk before the shortfall materialises. This paper evaluates that promise with a simulation of 40 business units observed weekly for a year, in which each unit’s KPI depends on its own history, on two leading indicators that act with a lag of two to three weeks, and on occasional negative shocks. A gradient-boosting forecaster trained on lagged KPI values and leading indicators reduces one-week-ahead RMSE by 11% relative to a last-value forecast and by 68% relative to the period mean, and an early-warning classifier for shocks two weeks ahead attains an AUC of 0.65. In a policy simulation comparing no intervention, reactive intervention triggered by an observed shortfall, and predictive intervention triggered by a forecast shortfall, the predictive policy delivers the highest mean KPI, although its margin over the reactive policy is modest relative to the gain from intervening at all. The paper proposes an evaluation framework for predictive BI with four tiers (forecast accuracy, warning quality, decision quality and outcome improvement) and argues that organisations should justify predictive BI investments on the basis of the third and fourth tiers rather than the first.