Asset Management DataInfrastructures

Abstract

Many organizations tasked with managing public utility infrastructure routinely collect and store large volumes of data for decision making purposes in their management and maintenance processes. This data is collected, stored and analyzed within asset management data infrastructures, however, traditional data management methods are becoming increasingly inadequate. More and more, data is being provided by new sources that can communicate over the internet, collectively known as the Internet of Things (IoT). IoT may benefit the management of public utility infrastructures by providing enough quality data to generate trusted information required to make the right decisions at the right time, helping asset management organizations improve their decision-making capability. The extensible asset management data infrastructure model presented in this dissertation aims at improving our understanding of asset management through IoT. Using a Duality of Technology lens, this research takes the view that IoT is continually being socially and physically constructed, and discriminates between human activity that affects IoT, and human activity that is affected by IoT. Explorative case studies in the asset management domain are used as the main research method. Taking the view that asset management data infrastructures are complex adaptive systems ensures that the resulting model is capable of dealing with the evolution of asset management data infrastructures in the face of new technologies and new requirements. The usability of the model is tested by means of test case studies. The tests indicate that the model can be used to improve our understanding of asset management through IoT and to provide actionable insights for the achievement of expected benefits and mitigation of risks of asset management through IoT.