Toward Quantifying Data Usage of Machine Data
Published in IFIP Working Group 5.7, Advances in Production Management Systems (APMS 2026), 2026
Recommended citation: Albers, A., Kober, C., Netland, T. (2027). Toward Quantifying Data Usage of Machine Data. In: Pezzotta, G., Gaiardelli, P., Cimini, C., Sala, R., Romero, D., Baalsrud-Hauge, J. (eds) Advances in Production Management Systems: Shaping the Future of Industry Through Sustainable, Data-Driven, and Human-Centric Production Systems. APMS 2026. IFIP Advances in Information and Communication Technology, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-032-38606-9_33 http://hdl.handle.net/20.500.11850/806438
Abstract
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Organisations are increasingly collecting large volumes of industrial machine data, yet much of this data remains unused for knowledge creation. Despite widespread claims that firms are “data-rich but information-poor”, there is a lack of systematic approaches to quantify how much of the generated data is actually used. In this paper, we propose a method to quantify and analyse machine data in industrial production systems. The artefact is developed and evaluated following a design-science approach in a case study at a food manufacturing company, where we analyse over 300,000 data points extracted from the SCADA system of a production line. Our results show that only a small fraction of the available data is persistently stored and potentially usable for knowledge generation, while the majority remains transient or unused. Furthermore, we identify key technical and organisational factors that shape how machine data can be repurposed, including data collection design, system constraints, and social practices. The proposed method enables scalable and systematic analysis of industrial data across production systems. It contributes to research on data value creation by providing a novel approach to quantify existing data usage and proposing a more nuanced evaluation of data usage needs.
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