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Abstract
The data that matters to executives in the United States industry includes machine performance data, maintenance and service data, and production data. This data improves machine performance, reduces downtime, and increases efficiency. The individuals who need this data include executives, maintenance and service personnel, and production managers. The methods of ensuring that the critical data reaches the users have industrial big data analytics, implementing a robust data management system, and training personnel. The use of big data analytics in various industries has been growing rapidly over the past few years. The industrial sector has seen significant benefits from implementing big data analytics. This research paper explores the potential of future maintenance and service innovation in the United States industrial sector using big data analytics, the benefits of being data-driven, and implementing a data-driven process strategy in the United States industrial sector. The paper explains the key data sources, storage, and processing techniques currently used in the industry to gather and analyze data. The paper also identifies the challenges and methodologies in leveraging big data analytics to drive maintenance and service operations innovation. The study will focus on identifying the data that matters most to executives in the industry, determining who needs it, and exploring methods for ensuring the critical data is effectively communicated to its intended users. Additionally, the paper will examine recent advances and terminologies in big data analytics, the methodology for designing innovation-based industries, presents the theoretical background and hypotheses, and examine limitations and future research opportunities in the field. KEYWORDS - Maintenance, Innovation, Big Data, Analytics, Industry, United States, Data, Organization DOI: 10.7176/CEIS/14-1-04 Publication date: February 28 th 2023
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