A common complaint heard within the engineering asset community is that while the capacity for data storage increases, the quality of ever increasing amounts of data remains poor. We propose a new model of engineering asset data management that helps explain why data collected by organizations frequently fails to assist in effective engineering asset management. The model situates a four component typology of engineering\ud data between institutional drivers (e.g. organizational culture; organizational strategy; organizational life-cycle; consequence of asset failure) and asset management outcomes. We argue these outcomes (regulatory compliance; time-based maintenance; condition-based asset management; capacity development) are functions\ud not only of the data collected by an organization, but its capacity to leverage that data. We develop a model suggesting that institutional drivers dictate the data requirements of engineering asset intensive firms, typically\ud at the cost of data requirements for different phases in the asset's life-cycle. This paper will assist practitioners to re-conceptualize the manner in which they view their data, the manner in which it is utilized, and provide a better understanding of data and its intended outcomes. This will allow a better prioritization of data collection\ud activities and offer an improved insight into ways in which engineering data may be better transformed into informational and knowledge outcomes
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