An Automated Data Validation Approach to Enterprise Asset Management for Power and Utilities Organizations

Abstract

Power and Utilities (P&U) organizations generate important data from Enterprise Asset Management (EAM) systems, which are used to help manage physical asset life cycle, operations, and related business processes. A range of physical asset types are used in power generation, transmission, and distribution. Asset Data Quality (ADQ) in EAM is one area which is often overlooked during EAM system implementation. The information quality focus has been on the final database, leading to rework and even persistent data quality deficiencies, thereby losing the significant benefits of enforcing data quality by designing data structures correctly at their source before data records are added to the database. Good quality asset data supports organizational objectives, where quality is associated with the “fitness for use” of data as the overarching, multidimensional perspective on the quality of the data. This is about the fitness of data for supporting operations, and distinct from the fitness for use of the equipment itself. A high quality asset data set also directly contributes to the decisions the asset owner must make to increase asset availability, optimize overall cost of asset maintenance, and reduce risks associated with asset operation. This paper proposes the Automatic Data Validation (ADV) Approach for validating fitness for use of asset data using three data quality dimensions: completeness, uniqueness, and consistency. An implementation, ADV Tool, is also presented as a proof of concept to show how it can benefit EAM business processes in P&U organizations by improving asset data quality.