Industrial IoT systems generate massive volumes of time-series data, but raw data is often messy. Missing values, anomalies, and stuck values (where sensors report constant readings) can corrupt downstream analytics and machine learning models. This guide covers practical detection methods, such as statistical thresholds and rolling windows, and cleaning strategies like interpolation, imputation, and flagging. It emphasizes the importance of domain-aware validation to avoid false positives. For data engineers and IoT developers, mastering these techniques ensures reliable data pipelines and better operational insights. The article also highlights trade-offs between different cleaning approaches, helping teams choose the right strategy for their specific use cases.
Learn how to handle common data quality issues in industrial IoT time-series data, including missing, anomalous, and stuck values, with practical detection and cleaning techniques.