Integrating a Project Risk Model into a BI Architecture

Marco Nunes (), António Abreu (), Jelena Bagnjuk (), Célia Saraiva () and Helena Viana ()
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Marco Nunes: Project Management Department
António Abreu: Polytechnic Institute of Lisbon, and CTS Uninova
Jelena Bagnjuk: UKE (University Medical Center-Eppendorf)
Célia Saraiva: UTAD-IST
Helena Viana: Commodity & Services Buyer at Supply Chain Department - BorgWarner,Lugar de Salvaterra

A chapter in Digital Transformation in Industry, 2022, pp 423-432 from Springer

Abstract: Abstract In today’s unpredictable and disruptive business landscape organizations face challenges that severely threatens their existence. To efficiently respond such challenges organizations must craft strategies to become more data-informed, agile, adaptative, and flexible. Integrating dynamic data analytical models in organizational structures to collect, analyze and interpret business data, is critical to organizations because it enables them to make more data-informed decisions and reduce bias in decision-making. In this work is illustrated the integration of a heuristic project risk-model used to identify project critical success factors into a typical organizational business intelligence architecture. The proposed integration enables organizations to efficiently and in a timely manner identify project collaborative risks by addressing people, environment, and tools, and generate actionable project-related knowledge that helps organizations to efficiently respond business challenges and achieve sustainable competitive advantages.

Keywords: Industry 4.0; BI architecture; Digital transformation; Project risk management; Artificial intelligence; Machine learning (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-94617-3_29

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DOI: 10.1007/978-3-030-94617-3_29

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