Factors Influencing Adoption of IoT for Data-driven Decision Making in Asset Management Organizations
Open accessAbstract
Organizations tasked with managing large scale, public civil infrastructure are increasingly looking at data to drive their asset management decision-making processes. The Internet of Things (IoT) enables the creation of data that can be used to gain further insights into the current and predicted state of the infrastructure and may help automate the asset management process. Yet, it remains unclear to what extent data from IoT impacts decision-making in public asset management organizations. The objective of this paper is to explore implementation factors for adoption of new data sources for decision-making in asset management organizations. Based on a systematic literature review and case studies in the asset management domain, this paper derives the current use and expectations of new data sources for decision-making in asset management. The paper concludes that although recent technological developments have enabled the deployment of IoT for asset management, the current level of adoption remains low. The inherent complexity of adopting a datadriven approach to asset management requires an effective data governance strategy to ensure data quality, manage expectations, build trust and integrate IoT data in decision-making processes.
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The results of data science initiatives are often not accepted by asset management organizations.
“The literature background reveals that the results of data science initiatives are often not accepted by asset management organizations (Brous et al. 2017).”
From Trusted Decision-Making: Data Governance for Creating Trust in Data Science Decision Outcomes (2020) · cites this paper as Brous et al. 2017
The indexed Abstract (page not available) says IoT adoption in asset management remains low and that building trust is important. It does not say that organizations often reject the results of data science initiatives. Searches for that wording and for acceptance of analytics results returned no passage addressing the claim. (LLM, conf=0.72)
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- Authors
- Brous, P., Janssen, M., Schraven, D., Spiegeler, J., & Can Duzgun, B.
- Affiliations
- Delft University of Technology (NL)
- Delft University of Technology, Netherlands
- Venue
- Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security
- Published
- 2017
- Pages
- 70-79
- OpenAlex
- W2613120196
- Cited by
- 41
- References
- 53
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 7 · 2023: 4 · 2022: 7 · 2021: 10 · 2020: 5 · 2019: 3 · 2018: 1
- FWCI
- 4.38
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- International Conference on Internet of Things, Big Data and Security (IoTBDS) (publishedVersion, cc-by-nc-nd)
- Research Repository (Delft University of Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
- Keywords
- Internet of Things, asset management, data-driven decision making, technology adoption, public infrastructure, data governance, data quality, civil infrastructure, IoT data integration, trust in data
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