The rise of Big, Open and Linked Data (BOLD) enables Big Data Algorithmic Systems (BDAS) which are often based on machine learning, neural networks and other forms of Artificial Intelligence (AI). As such systems are increasingly requested to make decisions that are consequential to individuals, communities and society at large, their failures cannot be tolerated, and they are subject to stringent regulatory and ethical requirements. However, they all rely on data which is not only big, open and linked but varied, dynamic and streamed at high speeds in real-time. Managing such data is challenging. To overcome such challenges and utilize opportunities for BDAS, organizations are increasingly developing advanced data governance capabilities. This paper reviews challenges and approaches to data governance for such systems, and proposes a framework for data governance for trustworthy BDAS. The framework promotes the stewardship of data, processes and algorithms, the controlled opening of data and algorithms to enable external scrutiny, trusted information sharing within and between organizations, risk-based governance, system-level controls, and data control through shared ownership and self-sovereign identities. The framework is based on 13 design principles and is proposed incrementally, for a single organization and multiple networked organizations.

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More and more, asset management organizations are introducing data science initiatives to support predictive maintenance and anomaly detection. Asset management organizations are by nature data intensive to manage their assets like bridges, dykes, railways and roads. For this, they often implement data lakes using a variety of architectures and technologies to store big data and facilitate data science initiatives. However, the decision-outcomes of data science models are often highly reliant on the quality of the data. The data in the data lake therefore has to be of sufficient quality to develop trust by decision-makers. Not surprisingly, organizations are increasingly adopting data governance as a means to ensure that the quality of data entering the data lake is and remains of sufficient quality, and to ensure the organization remains legally compliant. The objective of the case study is to understand the role of data governance as success factor for data science. For this, a case study regarding the governance of data in a data lake in the asset management domain is analyzed to test three propositions contributing to the success of using data science. The results show that unambiguous ownership of the data, monitoring the quality of the data entering the data lake, and a controlled overview of standard and specific compliance requirements are important factors for maintaining data quality and compliance and building trust in data science products.

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Organizations are increasingly looking to adopt the Internet of Things (IoT) to collect the data required for data-driven decision-making. IoT might yield many benefits for asset management organizations engaged in infrastructure asset management, yet not all organizations are equipped to handle this data. IoT data is collected, stored, and analyzed within data infrastructures and there are many changes over time, resulting in the evolution of the data infrastructure and the need to view data infrastructures as complex adaptive systems (CAS). Such data infrastructures represent information about physical reality, in this case about the underlying physical infrastructure. Physical infrastructures are often described and analyzed in literature as CASs, but their underlying data infrastructures are not yet systematically analyzed, whereas they can also be viewed as CAS. Current asset management data models tend to view the system from a static perspective, posing constraints on the extensibility of the system, and making it difficult to adopt new data sources such as IoT. The objective of the research is therefore to develop an extensible model of asset management data infrastructures which helps organizations implement data infrastructures which are capable of evolution and aids the successful adoption of IoT. Systematic literature review and an IoT case study in the infrastructure management domain are used as research methods. By adopting a CAS lens in the design, the resulting data infrastructure is extendable to deal with evolution of asset management data infrastructures in the face of new technologies and new requirements and to steadily exhibit new forms of emergent behavior. This paper concludes that asset management data infrastructures are inherently multilevel, consisting of subsystems, links, and nodes, all of which are interdependent in several ways.@en

The Internet of Things (IoT) might yield many benefits for organizations, but like other technology adoptions may also introduce unforeseen risks and requiring substantial organizational transformations. This paper analyzes IoT adoption by organizations, and identifies IoT benefits and risks. A Big, Open, Linked Data (BOLD) categorization of the expected benefits and risks of IoT is made by conducting a comprehensive literature study. In-depth case studies in the field of asset management were then executed to examine the actual experienced, real world benefits and risks. The duality of technology is used as our theoretical lens to understand the interactions between organization and technology. The results confirm the duality that gaining the benefits of IoT in asset management produces unexpected social changes that lead to structural transformation of the organization. IoT can provide organizations with many benefits, after having dealt with unexpected risks and making the necessary organizational changes. There is a need to introduce changes to the organization, processes and systems, to develop capabilities and ensure that IoT fits the organization's purposes.

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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. @en

Purpose: Managers are increasingly looking to adopt the Internet of Things (IoT) to include the vast amount of big data generated in their decision-making processes. The use of IoT might yield many benefits for organizations engaged in civil infrastructure management, but these benefits might be difficult to realize as organizations are not equipped to handle and interpret this data. The purpose of this paper is to understand how IoT adoption affects decision-making processes. Design/methodology/approach: In this paper the changes in the business processes for managing civil infrastructure assets brought about by IoT adoption are analyzed by investigating two case studies within the water management domain. Propositions for effective IoT adoption in decision-making processes are derived. Findings: The results show that decision processes in civil infrastructure asset management have been transformed to deal with the real-time nature of the data. The authors found the need to make organizational and business process changes, development of new capabilities, data provenance and governance and the need for standardization. IoT can have a transformative effect on business processes. Research limitations/implications: Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to test the propositions further. Practical implications: The paper shows that data provenance is necessary to be able to understand the value and the quality of the data often generated by various organizations. Managers need to adapt new capabilities to be able to interpret the data. Originality/value: This paper fulfills an identified need to understand how IoT adoption affects decision-making processes in asset management in order to be able to achieve expected benefits and mitigate risk.

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Organizations tasked with managing large scale, public civil infrastructure are increasingly looking at data to drive their asset management decision-making processes. The Internet of Things (IoT) enables the creation of data that can be used to gain further insights into the current and predicted state of the infrastructure and may help automate the asset management process. Yet, it remains unclear to what extent data from IoT impacts decision-making in public asset management organizations. The objective of this paper is to explore
implementation factors for adoption of new data sources for decision-making in asset management organizations. Based on a systematic literature review and case studies in the asset management domain, this paper derives the current use and expectations of new data sources for decision-making in asset management. The paper concludes that although recent technological developments have enabled the deployment of IoT for asset management, the current level of adoption remains low. The inherent complexity of adopting a datadriven approach to asset management requires an effective data governance strategy to ensure data quality, manage expectations, build trust and integrate IoT data in decision-making processes.@en

More and more data is becoming available and is being combined which results in a need for data governance - the exercise of authority, control, and shared decision making over the management of data assets. Data governance provides organizations with the ability to ensure that data and information are managed appropriately, providing the right people with the right information at the right time. Despite its importance for achieving data quality, data governance has received scant attention by the scientific community. Research has focused on data governance structures and there has been only limited attention given to the underlying principles. This paper fills this gap and advances the knowledge base of data governance through a systematic review of literature and derives four principles for data governance that can be used by researchers to focus on important data governance issues, and by practitioners to develop an effective data governance strategy and approach. @en

Organizations are increasingly looking to trusted data to drive their decision making process. Trusted data has a clear, defined and consistent quality which meets the expectations of the user. Data infrastructures which produce trusted data and provide organizations with the capability to make the right decisions at the right time are socio-technical networks, consisting of technical infrastructures and actor networks, and as such they are often complex and adaptive. Critical issues, challenges, and dilemmas can be identified while looking at data infrastructures as a socio-technical systems. This paper explores conditions and factors for effective and sustainable development of data infrastructures in organizations and suggests that the inherent complexity of data infrastructures requires a multi-faceted way of data governance. Several predefined components of data infrastructures which contain the behavior of agents through various coordination mechanisms have been developed to model the effect of data governance on data infrastructures. These components can be further customized to model an empirical situation more closely. Finally, the paper suggests institutionalization of data governance within an organization as a unifying concept towards the effectiveness and sustainability of data infrastructures, recognizing their inherent complexities. The approach is illustrated with a case study in the asset management domain.@en

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 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 decision-making. 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 management organizations. @en

More and more asset management organizations are relying on trusted data to drive their decision making process. Yet little systematic research has been performed regarding the generation of trusted data. Trusted data has a clear, defined and consistent quality which meets the expectations of the user. The aim of the research presented in this paper is to develop a conceptual model to support asset management organizations with regards to their development of data infrastructures which produce trusted data and provide organizations with the capability to make the right decisions at the right time. The autonomous characteristics of agent-based systems and process orientation of discrete-event simulation are combined in our conceptual model. In this way both the autonomous social behavior of organizations and their business processes can be modelled. Several predefined components containing the behavior of agents through various coordination mechanisms have been developed. These components can be further customized to model an empirical situation more closely. The approach is illustrated with a case study in the asset management domain.@en