Content area
Abstract
Every industry has significant data output as a product of its working processes. Power and utilities (P&U) are no exception. Power and utilities organizations generate a lot of data from different business operational systems that provide diverse inputs to a central enterprise asset management (EAM) system tasked with supporting management of physical assets over their life cycles. Physical assets which cover a range of asset types are used in the generation, transmission, and distribution of power. Asset data quality is one area which is often overlooked during the implementation of EAM systems because the focus is primarily on the final data output stored in the target EAM system and not the quality of the data obtained from the diverse, heterogeneous source systems. Asset data quality also directly contributes to the decisions that asset owners must make to increase asset availability, optimize overall cost of asset maintenance, and reduce risks associated with asset operation. Given that more and more asset data is continuously accumulated, and asset data quality being a nebulous term, this mainly qualitative research therefore introduces the Rule-Based Automatic Data Validation Framework (RBADV-F) approach to improving asset data quality focusing on the three data quality dimensions of completeness, uniqueness, and consistency. The rules, in the context of this research, describe the cases where the researcher and asset management practitioners, based on their expert knowledge, define relevant criteria through the use of templates for the purpose of addressing asset data quality issues. This research will contribute to the advancement of asset data quality during the implementation of enterprise asset management systems in power and utilities organizations.
