Damilola Ayodele Ojo
Effective project risk monitoring remains central to successful project delivery, yet traditional approaches based on static registers and qualitative assessments fail to reflect dynamic project performance. This study reviews how historical business performance data can be leveraged through Decision Intelligence (DI) and predictive analytics to enhance risk monitoring and inform future project planning. Drawing on literature across project management, business analytics, and DI, it identifies how metrics such as budget variance, schedule adherence, and resource utilization can support data-driven forecasting and proactive risk control. The paper proposes a Data-Driven Risk Intelligence Framework (DRIF) that integrates performance data, analytics, and iterative learning to transform risk management into an adaptive, continuously improving process. The findings highlight both the promise of DI-enabled risk systems and the lack of empirical validation and standardized models across sectors. The study calls for cross-disciplinary research to operationalize DI frameworks and establish unified metrics for predictive, evidence-based risk management.