Mikael Berndtsson
Research
Research interest includes business intelligence and analytics. In particular, data-driven organisations.
Editor-in-Chief of the International Journal of Artificial Intelligence (AI) in Business and Management (IJAIBM).
Associate editor of International Journal of Business Intelligence Research (IJBIR).
2026
Proceedings of the 59th Hawaii International Conference on System Sciences: Hyatt Regency Maui, January 6-9, 2026
2026. Conference paper.
2025
The 16th International Conference on Information, Intelligence, Systems and Applications 10-12 July 2025, University of the Aegean, Mytilene, Greece
2025. Conference paper. https://doi.org/10.1109/IISA66859.2025.11311263
2023
International Journal of Business Intelligence Research
2023. Article.
https://doi.org/10.4018/IJBIR.332813
Communications of the ACM
2023. Article.
https://doi.org/10.1145/3582075
2022
Proceedings of the 2022 Pre-ICIS SIGDSA Symposium
2022. Conference paper.
Journal of Information Systems and Technology Management
2022. Article.
https://doi.org/10.4301/s1807-1775202219017
2020
The AI Magazine
2020. Article. https://doi.org/10.1609/aimag.v41i3.5307
International Journal of Business Intelligence Research
2020. Article.
https://doi.org/10.4018/IJBIR.2020010101
Business Intelligence Journal
2020. Article.
2019
TDWI Upside
2019. Article.
2018
2017
2015
2014
2009
2008
2007
2006
2005
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1992
Finished projects
Increased use of data analysis
Gaining a competitive edge over rivals should be a key concern for all organisations. Organisations that have advanced their use of sophisticated data analysis (predictive analytics, prescriptive analytics) beyond pilot projects and identify themselves as data-driven tend to be industry leaders.
September 2022 - August 2023 67938BISON: Better decisions through Big Data
Big data has gained much interesting in recent years due to the rapid expansion of the massive amount of data that is available for solving different types of tasks within many different application domains. However, today's big data is still on a fairly low level of abstraction when it comes to complex decision support tasks, subject to e.g. high dimensionality and significant portions of uncertainty regarding which patterns to look for in the data.
October 2015 - September 2019 67938