TY - GEN
T1 - Integrating a Project Risk Model into a BI Architecture
AU - Nunes, Marco
AU - Abreu, António
AU - Bagnjuk, Jelena
AU - Saraiva, Célia
AU - Viana, Helena
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - BI architecture
KW - Digital transformation
KW - Industry 4.0
KW - Machine learning
KW - Project risk management
UR - http://www.scopus.com/inward/record.url?scp=85128956819&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-94617-3_29
DO - 10.1007/978-3-030-94617-3_29
M3 - Conference contribution
AN - SCOPUS:85128956819
SN - 978-3-030-94616-6
T3 - Lecture Notes in Information Systems and Organisation
SP - 423
EP - 432
BT - Digital Transformation in Industry
A2 - Kumar, Vikas
A2 - Leng, Jiewu
A2 - Akberdina, Victoria
A2 - Kuzmin, Evgeny
PB - Springer
CY - Cham
T2 - 3rd Annual International Scientific Conference on Digital Transformation in Industry: Trends, Management, Strategies, DTI 2021
Y2 - 29 October 2021 through 29 October 2021
ER -