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Industrial analytics – An overview

dc.contributor.authorGröger, Christian
dc.date.accessioned2022-11-22T09:48:32Z
dc.date.available2022-11-22T09:48:32Z
dc.date.issued2022
dc.description.abstractThe digital transformation generates huge amounts of heterogeneous data across the industrial value chain, from simulation data in engineering, over sensor data in manufacturing to telemetry data on product use. Extracting insights from these data constitutes a critical success factor for industrial enterprises, e. g., to optimize processes and enhance product features. This is referred to as industrial analytics, i. e., data analytics for industrial value creation. Industrial analytics is an interdisciplinary subject area between data science and industrial engineering and is at the core of Industry 4.0. Yet, existing literature on industrial analytics is fragmented and specialized. To address this issue, this paper presents a holistic overview of the field of industrial analytics integrating both current research as well as industry experiences on real-world industrial analytics projects. We define key terms, describe typical use cases and discuss characteristics of industrial analytics. Moreover, we present a conceptual framework for industrial analytics that structures essential elements, e. g., data platforms and data roles. Finally, we conclude and highlight future research directions.en
dc.identifier.doi10.1515/itit-2021-0066
dc.identifier.pissn2196-7032
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39753
dc.language.isoen
dc.publisherDe Gruyter
dc.relation.ispartofit - Information Technology: Vol. 64, No. 1-2
dc.subjectdata analytics
dc.subjectmachine learning
dc.subjectdata management
dc.subjectdata lake
dc.subjectindustry
dc.subjectmanufacturing
dc.titleIndustrial analytics – An overviewen
dc.typeText/Journal Article
gi.citation.endPage65
gi.citation.publisherPlaceBerlin
gi.citation.startPage55
gi.conference.sessiontitleSelf-Portrayals of GI Junior Fellows

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