Integrating a Project Risk Model into a BI Architecture

Marco Nunes, António Abreu, Jelena Bagnjuk, Célia Saraiva, Helena Viana

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationDigital Transformation in Industry
Subtitle of host publicationDigital Twins and New Business Models
EditorsVikas Kumar, Jiewu Leng, Victoria Akberdina, Evgeny Kuzmin
Place of PublicationCham
PublisherSpringer
Pages423-432
Number of pages10
ISBN (Electronic)978-3-030-94617-3
ISBN (Print)978-3-030-94616-6
DOIs
Publication statusPublished - 2022
Event3rd Annual International Scientific Conference on Digital Transformation in Industry: Trends, Management, Strategies, DTI 2021 - Ekaterinburg, Russian Federation
Duration: 29 Oct 202129 Oct 2021

Publication series

NameLecture Notes in Information Systems and Organisation
PublisherSpringer
Volume54
ISSN (Print)2195-4968
ISSN (Electronic)2195-4976

Conference

Conference3rd Annual International Scientific Conference on Digital Transformation in Industry: Trends, Management, Strategies, DTI 2021
Country/TerritoryRussian Federation
CityEkaterinburg
Period29/10/2129/10/21

Keywords

  • Artificial intelligence
  • BI architecture
  • Digital transformation
  • Industry 4.0
  • Machine learning
  • Project risk management

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