Collaboration-Based Automatic Data Validation Framework for Enterprise Asset Management

Abstract

Automatic Data Validation (ADV) is critical for the effective and successful implementation of Enterprise Asset Management (EAM) systems in the Power and Utilities (P&U) and other asset-intensive industries as part of their digital transformation initiatives. During such implementations, data is consolidated, standardized and integrated from multiple systems. The exclusion of ADV explains why data quality issues are encountered during the project phase and after deployment of the EAM system. Enforcing ADV in EAM will directly contribute to asset data quality which is defined by a set of dimensions such as completeness, objectivity, relevancy, reputation, timeliness, accuracy, and consistency. This research is proposing a framework called Collaboration-Based Automatic Data Validation Framework for Enterprise Asset Management (CBADVFEAM) to complement the traditional data Extraction, Transformation and Loading (ETL) process. The research introduces data domains that emphasize direct engagement with the asset management stakeholders in the early stages of EAM system implementations. The CBADVFEAM framework will also deploy an intelligent toolset based on an algorithm that (a) detect data anomalies from distributed, heterogeneous data sources, (b) automatically validate the accuracy, and (c), report on the variances. Finally, this research will set the stage for future studies on the importance of ADV during the implementation of EAM solutions in the P&U industry and thus raise general awareness of data quality problems.