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Review Article

A Data-Driven Framework for Project Risk Monitoring Using Decision Intelligence and Predictive Analytics

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Abstract

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.

Keywords:

Business Performance Data Data-Informed Project Planning Decision Intelligence Lessons Learned Predictive Analytics Project Forecasting Project Risk Monitoring

Article information

Journal

Journal of Management, and Development Research

Volume (Issue)

2(2), (2025)

Pages

125-136

Published

10-11-2025

How to Cite

Ojo, D. A. (2025). A Data-Driven Framework for Project Risk Monitoring Using Decision Intelligence and Predictive Analytics. Journal of Management, and Development Research, 2(2), 125-136. https://doi.org/10.69739/jmdr.v2i2.1171

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