Data Quality (DQ) is seen as critical to effective business decision-making. However, maintaining DQ is often acknowledged as problematic. Asset data is the key enabler in gaining control of assets. The quality asset data provides the foundation for effective Asset Management (AM). Researches have indicated that achieving AM DQ is the key challenge engineering organisations face today. This paper investigates the DQ issues emerging from the unique nature of engineering AM data. It presents an exploratory research of a large scale national-wide DQ survey on how Australian engineering organisations address DQ issues, and proposes an AM specific DQ framework.

The implementation of a BI system is a complex undertaking requiring considerable resources. Yet there is a limited authoritative set of CSFs for management reference. This article represents a first step of filling in the research gap. The authors utilized the Delphi method to conduct three rounds of studies with 15 BI system experts in the domain of engineering asset management organizations. The study develops a CSFs framework that consists of seven factors and associated contextual elements crucial for BI systems implementation. The CSFs are committed management support and sponsorship, business user-oriented change management, clear business vision and well-established case, business-driven methodology and project management, business-centric championship and balanced project team composition, strategic and extensible technical framework, and sustainable data quality and governance framework. This CSFs framework allows BI stakeholders to holistically understand the critical factors that influence implementation success of BI systems.

The learning analytics community has matured significantly over the past few years as a middle space where technology and pedagogy combine to support learning experiences. To continue to grow and connect these perspectives, research needs to move beyond the level of basic support actions. This means exploring the use of data to prove richer forms of actions, such as personalized feedback, or hybrid approaches where instructors interpret the outputs of algorithms and select an appropriate course of action. This paper proposes the following three contributions to connect data extracted from the learning experience with such personalized student support actions: 1) a student-instructor centred conceptual model connecting a representation of the student information with a basic set of rules created by instructors to deploy Personalized Learning Support Actions (PLSAs); 2) a software architecture based on this model with six categories of functional blocks to deploy the PLSAs; and 3) a description of the implementation of this architecture as an open-source platform to promote the adoption and exploration of this area. Notes for Practice• The report draws on research findings related to the effect of personalized feedback on student satisfaction and academic performance (Pardo, Jovanović, Dawson, Gašević, & Mirriahi, 2018).• The main contribution is the description of the design and implementation of an open source platform for researchers and practitioners to connect data with personalized learning support actions.• The area of learning analytics needs tools such as the one described in this document to serve as a vehicle to exchange insights among researchers and practitioners.• This is an example of the note for practice and research

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Abstract. This paper presents the results of three rounds Delphi study with 15 BI systems experts in the domain of engineering asset management The study provides a CSF framework that consists of seven dimensions and 22 factors crucial for successful BI system implementation. The seven critical dimensions of CSFs are management commitment and championship, user-oriented change management, business vision, project planning, team skills and composition, data and infrastructure-related dimensions. These findings allow BI stakeholders to optimize their scarce resources on those key areas that are most likely to have an impact on the implementation of the BI systems. Keywords: Critical success factors. Business analytics. Business intelligence system. Strategic information systems, Delphi method \. BACKGROUNDEngineering asset management organisations (EAMOs), such as utilities and transportation enterprises, store vast amounts of asset-oriented data. However, the data and information environments in these organisations are typically fragmented and characterized by disparate operational, transactional and legacy systems spread across multiple platforms and diverse structures [1]. An ever-increasing amount of such data is often collected for immediate use in assessing the operational health of an asset, and then it is either archived or deleted. This lack of vertical integration of information systems, together with the pools of data spread across the enterprise, makes it extremely difficult for management to make well-informed decisions thus resulting in suboptimal management performance. Moreover, with the many millions of dollars of investment in ERP-style systems, engineering enterprises have been storing large volumes of transactional data, leading to increased difficulties in analyzing, summarizing and extracting reliable information. Meantime, increased regulatory compliance and governance requirements have demanded greater accountability for decision making within such organisations. Bad decisions resulting from poor quality information may have significant financial and reputation implications. In response to these problems, many EAMOs are compelled to improve Please use the following format when citing this chapter:

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