Mikael Berndtsson

Education
Course Coordinator
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).
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.
Business Intelligence Journal
2019. Article.
2018
26th European Conference on Information Systems (ECIS2018), Beyond Digitization - Facets of Socio-Technical Change, Portsmouth, United Kingdom, June 23-28, 2018
2018. Conference paper.
2017
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Ongoing projects
Real-time environmental feedback
According to international research, real-time environmental feedback can reduce electricity or water consumption by up to 50%. This project will investigate how it can be applied in Hjo Municipality.
April 2023 - September 2025 Informatics,Organising for Sustainable DevelopmentFinished 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 InformaticsBISON: 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 Informatics