Coordinating Data-Driven Decision-Making in Public Asset Management Organizations: A Quasi-Experiment for Assessing the
Public organizations are facing increasing challenges to the management of their infrastructure assets. New sources of data, such as social media and IoT, can provide new insights for organizations to help them deal with these challenges. Yet data must be
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Delft University of Technology, Delft, The Netherlands {P.A.Brous,M.F.W.H.A.Janssen,P.M.Herder}@tudelft.nl
Abstract. Public organizations are facing increasing challenges to the manage‐ ment of their infrastructure assets. New sources of data, such as social media and IoT, can provide new insights for organizations to help them deal with these challenges. Yet data must be of sufficient quality in order to be acted upon. The objective of this study is to develop and approach to evaluate how data governance improves decision-making in asset management organizations. This paper describes a quasi-experiment which identifies and quantifies relationships between data governance and improvements in asset management decisionmaking. The quasi-experiment focusses on data requirements for determining current and future asset conditions, which is critical for assessing remaining service life and risk of failure. The quasi-experiment utilizes a pre-test post-test control group design. We expect that the inclusion of data governance improves the quality of data which allows for improved decision-making in asset manage‐ ment organizations. Keywords: Data · Data governance · Data quality · Data management · Experiment
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Introduction
Public asset management organizations are facing increasing challenges to the manage‐ ment of their infrastructure assets, technological advances, political shifts, changing stakeholders, or economic fluctuations. Many public asset management (AM) organi‐ zations routinely store large volumes of data in an attempt to find ways to improve efficiency and effectiveness of their AM processes through data-driven decision-making [8, 15]. Increasing the complexity is the development of techniques which utilise other data sources such IoT and Social Media data to provide information which may provide more timely information than more traditional methods. We follow Mohseni’s [24] definition of AM as being a discipline for optimizing and applying strategies related to work planning decisions in order to effectively and efficiently meet the desired objective [17, 22, 24]. AM is therefore essentially a matter of understanding risk, followed by © IFIP International Federation for Information Processing 2016 Published by Springer International Publishing Switzerland 2016. All Rights Reserved Y.K. Dwivedi et al. (Eds.): I3E 2016, LNCS 9844, pp. 573–583, 2016. DOI: 10.1007/978-3-319-45234-0_51
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developing and applying the correct business strategy, and the right organization, process and technology models to solve the problem [24]. This study is centered on the AM process of determining current and future asset conditions, which is critical for assessing the remaining service life of assets and to prevent the risk of failure of assets. This knowledge has a direct impact on decisions regarding the provision of logistic and maintenance support for assets and disposing of, or renewing assets. The objective of this study is to evaluate how data governance supports data-driven decision-making in asset manage
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