Start Date
10-12-2017 12:00 AM
Description
The Internet of Things (IoT) challenges companies to reinforce their digital strategy. For example, connected vehicles shape the future of the automotive industry by enabling new services, which will become integral to vehicles’ value. By applying predictive maintenance, after-sales services can prevent forced outages. This requires a digital service platform that enables predictive maintenance. For such a platform, we deduce requirements to overcome the inherent challenges of IoT data. Based on these, we propose a suitable architecture, particularly focusing on data quality criteria by developing several pipelines for data cleaning. We exemplify the platform's value creation via its data analytics functions. Thus, this paper demonstrates excavating the treasure of IoT data and outlines a transferable architecture to empower rapid data analytics for better business decision making and enabling business innovations.
Recommended Citation
Gerloff, Christian and Cleophas, Catherine, "Excavating the Treasure of IoT Data: An Architecture to Empower Rapid Data Analytics for Predictive Maintenance of Connected Vehicles" (2017). ICIS 2017 Proceedings. 23.
https://aisel.aisnet.org/icis2017/DataScience/Presentations/23
Excavating the Treasure of IoT Data: An Architecture to Empower Rapid Data Analytics for Predictive Maintenance of Connected Vehicles
The Internet of Things (IoT) challenges companies to reinforce their digital strategy. For example, connected vehicles shape the future of the automotive industry by enabling new services, which will become integral to vehicles’ value. By applying predictive maintenance, after-sales services can prevent forced outages. This requires a digital service platform that enables predictive maintenance. For such a platform, we deduce requirements to overcome the inherent challenges of IoT data. Based on these, we propose a suitable architecture, particularly focusing on data quality criteria by developing several pipelines for data cleaning. We exemplify the platform's value creation via its data analytics functions. Thus, this paper demonstrates excavating the treasure of IoT data and outlines a transferable architecture to empower rapid data analytics for better business decision making and enabling business innovations.