Data Gathering Challenges

Abstract

Enrico and Fred discuss discuss the challenges of data collection in reliability engineering, prompted by a listener working in asset reliability. They explore why gathering useful data is often the most difficult step, how modern connected systems are changing data availability, and why defining the purpose of analysis is essential before collecting data. The discussion also highlights organizational barriers, data quality issues, and the growing role of AI in supporting data analysis.

Key Points

Join Fred and Enrico as they discuss the challenges and strategies for collecting and using reliability data.

Topics include:

Data collection as a bottleneck: Gathering useful reliability data is often the most time-consuming and difficult step. Even when data exists, it is frequently incomplete, inconsistent, or requires extensive cleaning before it can be used.

Start from the question: Data collection should always be driven by a clear objective. Without a defined question or decision to support, collecting data becomes inefficient and often useless.

Organizational misalignment: Data is typically collected by different functions for different purposes, and reliability needs are often not considered. Aligning stakeholders and demonstrating value is essential to improve data availability and quality.

AI as an rnabler: New AI tools can significantly accelerate data cleaning, analysis, and exploration. However, they do not replace the need for clear problem definition, engineering judgment, and a solid understanding of the system.

Enjoy an episode of Speaking of Reliability. Where you can join friends as they discuss reliability topics. Join us as we discuss topics ranging from design for reliability techniques to field data analysis approaches.