Field maintenance data is often captured manually and is prone to have incomplete and inaccurate information in the structured fields.  However, unstructured fields captured through work order planning, scheduling, and execution contains a wealth of historical information about asset performance, failure patterns, and maintenance strategies.  The prevalent data quality issues in maintenance data need to be understood and processed in order to extract actionable intelligence.  This paper describes a best practices framework for measuring and improving data quality, developed through years of research and working with 120+ process and manufacturing organizations.  The framework enables evaluating and executing analytics by identifying strengths in the data.  It determines where and how asset performance measures such as benchmarking metrics, reliability measures, and bad actor identification can be evaluated with confidence.  Missing or inconsistent information can be extracted from the unstructured fields using natural language processing (NLP) techniques to bridge gaps in the analysis.  While the NLP algorithms make historical data usable for some analytics, the best practices identify improvements in the work process of capturing data, thereby improving future quality.  A feedback on data quality indicators completes the loop to sustain improvements.

A standardized taxonomy enables asset-intensive industrial organizations to systematically measure and track efficiency and performance of assets at different levels in an asset hierarchy. Having a well-structured taxonomy also allows companies to take advantage of emerging data-driven technologies such as PHM through enabling straightforward mapping of assets to analytical content specific to equipment commonalities, e.g., failure modes. However, the complexity and use of equipment taxonomy and coding structures in maintenance management systems vary widely for different organizations. This paper describes a data-driven approach for identifying equipment taxonomy from equipment records in maintenance management systems. The approach combines machine learning-based and rule-based methods into a hybrid man-in-the-loop workflow, which enables rapid and consistent mapping of equipment into a standard taxonomy. A case study is presented to demonstrate the performance and challenges of the proposed approach on equipment taxonomy classification.