Adopting Data-Analytics Methods for Decision-making Processes in IT Project Portfolio Management

Abstract

This paper investigates how data analytics can be operationalised to improve decision making in IT Project Portfolio Management (ITPPM) for large, scaled agile organisations, using the Disciplined Agile Delivery (DAD) portfolio process blade as the organising lens. Through a multivocal literature review that integrates peer-reviewed and credible grey literature, the research addresses three linked questions: 1) What challenges inhibit data-driven decision-making in ITPPM; 2) Which data analytics methods are reported for ITPPM; and 3) What are the benefits and success factors when analytics methods are integrated into portfolio practices. Findings show recurring barriers across DAD practices, led by poor data quality and integration, immature governance and PMO alignment, tooling and capability gaps, unreliable intake scoring, limited portfolio risk telemetry, and weaknesses in financial and operational measurement. To operationalise these insights, the study proposes a three-layer conceptual framework (DAD practices → analytics methods → benefits & success factors) that serves as a guide for leaders to sequence interventions: establish data and governance foundations, pilot targeted analytics, then institutionalise successful patterns. The paper concludes with practical roadmaps and research directions to validate the framework empirically.