Data-Driven Project Controls: The Convergence of EVM, Business Intelligence, and Real-Time Performance Visualization

Abstract

This paper examines how contemporary project-control practice is being transformed by the integration of Earned Value Management, Business Intelligence, predictive analytics, and real-time performance visualization. Its purpose is to clarify how these interrelated capabilities strengthen cost, schedule, risk, resource, and governance visibility in complex project environments. The study adopts a conceptual review approach, drawing on scholarly and professional literature on project controls, performance measurement, analytics, data architecture, cybersecurity, forecasting, and digital transformation. Through this approach, the paper synthesises existing knowledge to explain the technical, organisational, and strategic conditions required for project-control systems to become more predictive, transparent, and decision-oriented. The review finds that Earned Value Management remains a foundational framework for measuring cost and schedule performance, but its value increases substantially when integrated with Business Intelligence platforms and visual analytics. Business Intelligence enables the consolidation of dispersed project data, while real-time dashboards translate complex indicators into accessible intelligence for managers, executives, contractors, and other stakeholders. The study further finds that predictive analytics and early-warning systems can improve forecasting accuracy, reveal emerging delivery risks, and support corrective action before deviations become irreversible. However, these benefits depend on reliable data architecture, strong governance, cybersecurity assurance, employee readiness, and a culture that supports evidence-based decision-making. The paper concludes that modern project controls should evolve from retrospective reporting into strategic performance governance. It recommends that organisations standardise control metrics, invest in interoperable digital systems, strengthen data governance, develop analytical competencies, and embed human-centred automation into formal decision processes to improve delivery certainty and organisational accountability across diverse, data-intensive project-based organisations and institutional delivery contexts.