Podcast - Driving Asset Answers with Suspect Data

In episode 276 of the Rooted in Reliability podcast, I joined Yanpei Chao to explore how organizations can extract meaningful asset insights even when dealing with incomplete or suspect data. We discussed the root causes of poor data quality in asset management systems, including inconsistencies in data entry, system integrations, and legacy processes. Despite these challenges, we emphasized the importance of initiating analysis rather than waiting for perfect data, as actionable insights can still be derived using statistical methods, AI-driven data cleansing, and contextual understanding. We also covered best practices for improving data reliability over time, such as implementing structured data governance, standardizing data collection processes, and leveraging machine learning models to fill in gaps. This conversation provided practical strategies for organizations to make informed maintenance and operational decisions, even in the face of data uncertainty.

Full episode here: https://accendoreliability.com/podcast/rir/276-driving-asset-answers-with-suspect-data-with-manjish-and-yanpei/