Implementing Big Data-Driven Predictive Maintenance for Enhanced Efficiency in IoT Devices

The proliferation of loT devices in industrial settings has created unprecedented challenges in maintenance management, with organizations needing help to optimize device performance across large-scale deployments. This research examines the implementation of big data-driven predictive maintenance strategies as a solution to these challenges, addressing data quality, system integration, and resource allocation issues. Through analysis of multiple industry implementations and case studies, the research demonstrates that predictive maintenance strategies, when properly implemented with machine learning algorithms and robust data collection frameworks, can reduce maintenance costs by up to 30% and improve equipment reliability by 70%. The findings suggest that successful implementation depends on technical infrastructure and organizational readiness, with standardized data protocols and skilled workforce development being critical success factors.