Business Intelligence & Analytics and Decision Quality - Insights on Analytics Specialization and Information Processing Modes

Leveraging the benefits of business intelligence and analytics (BI&A) and improving decision quality does not only depend on establishing BI&A technology, but also on the organization and characteristics of decision processes. This research investigates new perspectives on these decision processes and establishes a link between characteristics of BI&A support and decision makers’ modes of information processing behavior, and how these ultimately contribute to the quality of decision outcomes. We build on the heuristic–systematic model (HSM) of information processing, as a central explanatory mechanism for linking BI&A support and decision quality. This allows us examining the effects of decision makers’ systematic and heuristic modes of information processing behavior in decision making processes. We further elucidate the role of analytics experts in influencing decision makers’ utilization of analytic advice. The analysis of data from 136 BI&A-supported decisions reveals how high levels of analytics elaboration can have a negative effect on decision makers’ information processing behavior. We further show how decision makers’ systematic processing contributes to decision quality and how heuristic processing restrains it. In this context we also find that trustworthiness in the analytics expert plays an important role for the adoption of analytic advice

Toward a Model Undergraduate Curriculum for the Emerging Business Intelligence and Analytics Discipline

Business intelligence (BI) combined with business analytics (BA) is an increasingly prominent strategic objective for many organizations. As a pedagogical subject, BI/BA is still in its infancy, and, in order for this to mature, we need to develop an undergraduate model BI/BA curriculum. BI/BA as an academic domain is emerging as a hybrid of disciplines, including information systems, statistics, management science, artificial intelligence, computer science, and business practice/theory. Based on IS 2010’s model curriculum constructs (Topi et al., 2010), we explore two curricular options: a BI/BA concentration in a typical IS major and a comprehensive, integrated BI/BA undergraduate major. In support, we present evidence of industry need for BI/BA, review the current state of BI/BA education, and compare anticipated requirements for BI/BA curricula with the IS 2010 model curriculum. For this initial phase of curricular design, we postulate a preliminary set of knowledge areas relevant for BI/BA pedagogy in a multi-disciplinary framework. Then we discuss avenues for integrating these knowledge areas to develop professionally prepared BI/BA specializations at the undergraduate level. We also examine implications for both AACSB and ABET accreditation and describe the next phase of applying the IS 2010 concept structure to BI/BA curriculum development

Factors Influencing the Quality of Decision-Making Using Business Intelligence in a Metal Rolling Plant in KwaZulu-Natal

The current study sought to investigate the factors which influence the quality of decision-making using business intelligence (BI) in a metal rolling plant in KwaZulu-Natal. Specifically, the study was focused on information quality, system quality and BI service quality. The study used a self-administered survey sent out to participants having sufficient report runs which made up the population of the study. The collected data came from different levels of employees, namely; managers (47%) and non-managers (53%) with varying levels of BI experience, and the data was imported into SPSS for analysis. The results showed that information quality had a positive significant impact on the quality of decision-making; system quality had a positive significant impact on the quality of decision-making; and BI service quality had a positive significant impact on the quality of decision-making. Multiple linear regression analysis was conducted to determine the strength of these variances in influencing decision-making. It was found that the three variables explained 65.7% of the variance in the quality of decision-making. Overall, the study found that high quality information, coupled with a high-quality system and good BI service, leads to a higher quality of decision-making, and that the impact of BI on decision-making is positive. The study recommends that the company implement data quality management focusing on data cleansing, it should also implement more sophisticated analysis techniques to get insights and have strategies to upskill both technical and business workers

Business intelligence to support NOVA IMS academic services BI system

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceKimball argues that Business Intelligence is one of the most important assets of any organization, allowing it to store, explore and add value to the organization’s data which will ultimately help in the decision making process. Nowadays, some organizations and, in this specific case, some schools are not yet transforming data into their full potential and business intelligence is one of the most known tools to help schools in this issue, seen as some of them are still using out-dated information systems, and do not yet apply business intelligence techniques to their increasing amounts of data so as to turn it into useful information and knowledge. In the present report, I intend to analyse the current NOVA IMS academic services data and the rationales behind the need to work with this data, so as to propose a solution that will ultimately help the school board or the academic services to make better-supported decisions. In order to do so, it was developed a Data Warehouse that will clean and transform the source database. Another important step to help the academic services is to present a series of reports to discover information in the decision making process

Build Your Dream (not just Big) Analytics Program

This paper reports on a panel discussion held at AMCIS 2014 and subsequent panel member research and findings. We focus on curriculum design, program development, and sustainability in business analytics (BA) in higher education. We address some of the burning questions the IS community has asked concerning the various stages of BA program building, and we elaborate challenges that institutions face in constructing successful and competitive analytics programs. Furthermore, given that the panelists have achieved outstanding accomplishments in academic and industrial leadership, we share our experiences and vision of a “dream” analytics program. We hope that our community will continue a dialog that encourages and engages faculty members and administrators to reflect on challenges and opportunities to build dream programs that meet industry needs

MaCuDE IS Task Force Phase II Report: Views of Industry Leaders on Big Data Analytics and AI

This paper represents the Phase II report of the Management Curriculum for the Digital Era (MaCuDE) disciplinary task force on information systems (IS). Aligned with the current work of the AIS (Association for Information Systems) and ACM (Association for Computing Machinery), we focus on the current and future industry driven educational needs and requirements posed by big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and related innovations. In this report, we probe and report on the views of industry leaders regarding BDA/AI education needs. We conducted 18 rich semi-structured interviews with a representative sample of industry leaders around key changes and issues related to workforce demands in digital transformation and associated educational needs. We performed a grounded theory based analysis of key themes in reported education needs. We note the shifting meaning of AI and BDA phenomena and identify three main organizational level needs for the digital era -capability improvement and transformation, decision-making strategies and tactics, and changes in operations or products- and connect them to three individual professional competencies- fundamental environmental competencies, data information and content, and system design competencies- necessary to deliver them. Based on the analysis we outline several novel competency-based IS curriculum recommendations for the master\u27s and undergraduate level IS education

Data Analytics (Ab)Use in Healthcare Fraud Audits

This study explores how government-adopted audit data analytic tools promote the abuse of power by auditors enabling politically sensitive processes that encourage industry-wide normalization of behavior. In an audit setting, we investigate how a governmental organization enables algorithmic decision-making to alter power relationships to effect organizational and industry-wide change. While prior research has identified discriminatory threats emanating from the deployment of algorithmic decision-making, the effects of algorithmic decision-making on inherently imbalanced power relationships have received scant attention. Our results provide empirical evidence of how systemic and episodic power relationships strengthen each other, thereby enabling the governmental organization to effect social change that might be too politically prohibitive to enact directly. Overall, the results suggest that there are potentially negative effects caused by the use of algorithmic decision-making and the resulting power shifts, and these effects create a different view of the level of purported success attained through auditor use of data analytics

An intermunicipal integrated analytical territorial intelligence platform

Simões, P., de Castro Neto, M., Sarmento, P., & Barriguinha, A. (2023). Oeste smart region: An intermunicipal integrated analytical territorial intelligence platform. Mapping, 32(211), 50-61. [5]. https://doi.org/10.59192/mapping.395---This work was funded by the European Union under the European Regional Development Fund through the financing programs Compete 2020 and Portugal 2020.Smart regions are described as an instrument to achieve sustainable planning at the regional level, promoting knowledge-based development through learning as an integral part of the development of regional resources that solves challenges through the knowledgeable application of new technologies, the organization of processes and reasonable and future-proof decision-making. With this work we intend to present a territorial intelligence platform, in particular the spatial data infrastructure that supports it. Based on the potential of multiple sources and formats of data available (Big Data), from the systems of twelve Portuguese municipalities. Along with the Internet of Things and collective intelligence the developed model, sets out as an ambition to take advantage of the potential of data science and artificial intelligence, to promote a regional model of governance based on the management of information capable of leveraging the creation of a territorial intelligence center constituting a new paradigm of territorial planning and management based on facts. ___ Las regiones inteligentes se describen como un instrumento para lograr una planificación sostenible a nivel regional, promoviendo el desarrollo basado en el conocimiento a través del aprendizaje continuo como parte integral del desarrollo de los recursos regionales que resuelve los desafíos a través de la aplicación con conocimiento de las nuevas tecnologías, la organización de procesos y toma de decisiones razonables y preparadas para el futuro. Con este trabajo pretendemos presentar una plataforma de inteligencia territorial, en particular la infraestructura de datos espaciales que la soporta. Basado en el potencial de múltiples fuentes y formatos de datos disponibles (Big Data), de los sistemas de doce municipios portugueses. Junto con el Internet de las Cosas y la inteligencia colectiva, el modelo desarrollado se plantea como una ambición de aprovechar el potencial de la ciencia de datos y la inteligencia artificial, para impulsar un modelo regional de gobernanza basado en la gestión de la información capaz de impulsar la creación de un centro de inteligencia territorial que constituye un nuevo paradigma de planificación y gestión territorial basada en hechos.publishersversionpublishe

Data Science and Analytics in Industrial Maintenance: Selection, Evaluation, and Application of Data-Driven Methods

Data-driven maintenance bears the potential to realize various benefits based on multifaceted data assets generated in increasingly digitized industrial environments. By taking advantage of modern methods and technologies from the field of data science and analytics (DSA), it is possible, for example, to gain a better understanding of complex technical processes and to anticipate impending machine faults and failures at an early stage. However, successful implementation of DSA projects requires multidisciplinary expertise, which can rarely be covered by individual employees or single units within an organization. This expertise covers, for example, a solid understanding of the domain, analytical method and modeling skills, experience in dealing with different source systems and data structures, and the ability to transfer suitable solution approaches into information systems. Against this background, various approaches have emerged in recent years to make the implementation of DSA projects more accessible to broader user groups. These include structured procedure models, systematization and modeling frameworks, domain-specific benchmark studies to illustrate best practices, standardized DSA software solutions, and intelligent assistance systems. The present thesis ties in with previous efforts and provides further contributions for their continuation. More specifically, it aims to create supportive artifacts for the selection, evaluation, and application of data-driven methods in the field of industrial maintenance. For this purpose, the thesis covers four artifacts, which were developed in several publications. These artifacts include (i) a comprehensive systematization framework for the description of central properties of recurring data analysis problems in the field of industrial maintenance, (ii) a text-based assistance system that offers advice regarding the most suitable class of analysis methods based on natural language and domain-specific problem descriptions, (iii) a taxonomic evaluation framework for the systematic assessment of data-driven methods under varying conditions, and (iv) a novel solution approach for the development of prognostic decision models in cases of missing label information. Individual research objectives guide the construction of the artifacts as part of a systematic research design. The findings are presented in a structured manner by summarizing the results of the corresponding publications. Moreover, the connections between the developed artifacts as well as related work are discussed. Subsequently, a critical reflection is offered concerning the generalization and transferability of the achieved results. Thus, the thesis not only provides a contribution based on the proposed artifacts; it also paves the way for future opportunities, for which a detailed research agenda is outlined.:List of Figures List of Tables List of Abbreviations 1 Introduction 1.1 Motivation 1.2 Conceptual Background 1.3 Related Work 1.4 Research Design 1.5 Structure of the Thesis 2 Systematization of the Field 2.1 The Current State of Research 2.2 Systematization Framework 2.3 Exemplary Framework Application 3 Intelligent Assistance System for Automated Method Selection 3.1 Elicitation of Requirements 3.2 Design Principles and Design Features 3.3 Prototypical Instantiation and Evaluation 4 Taxonomic Framework for Method Evaluation 4.1 Survey of Prognostic Solutions 4.2 Taxonomic Evaluation Framework 4.3 Exemplary Framework Application 5 Method Application Under Industrial Conditions 5.1 Conceptualization of a Solution Approach 5.2 Prototypical Implementation and Evaluation 6 Discussion of the Results 6.1 Connections Between Developed Artifacts and Related Work 6.2 Generalization and Transferability of the Results 7 Concluding Remarks Bibliography Appendix I: Implementation Details Appendix II: List of Publications A Publication P1: Focus Area Systematization B Publication P2: Focus Area Method Selection C Publication P3: Focus Area Method Selection D Publication P4: Focus Area Method Evaluation E Publication P5: Focus Area Method ApplicationDatengetriebene Instandhaltung birgt das Potential, aus den in Industrieumgebungen vielfältig anfallenden Datensammlungen unterschiedliche Nutzeneffekte zu erzielen. Unter Verwendung von modernen Methoden und Technologien aus dem Bereich Data Science und Analytics (DSA) ist es beispielsweise möglich, das Verhalten komplexer technischer Prozesse besser nachzuvollziehen oder bevorstehende Maschinenausfälle und Fehler frühzeitig zu erkennen. Eine erfolgreiche Umsetzung von DSA-Projekten erfordert jedoch multidisziplinäres Expertenwissen, welches sich nur selten von einzelnen Personen bzw. Einheiten innerhalb einer Organisation abdecken lässt. Dies umfasst beispielsweise ein fundiertes Domänenverständnis, Kenntnisse über zahlreiche Analysemethoden, Erfahrungen im Umgang mit verschiedenen Quellsystemen und Datenstrukturen sowie die Fähigkeit, geeignete Lösungsansätze in Informationssysteme zu überführen. Vor diesem Hintergrund haben sich in den letzten Jahren verschiedene Ansätze herausgebildet, um die Durchführung von DSA-Projekten für breitere Anwendergruppen zugänglich zu machen. Dazu gehören strukturierte Vorgehensmodelle, Systematisierungs- und Modellierungsframeworks, domänenspezifische Benchmark-Studien zur Veranschaulichung von Best Practices, Standardlösungen für DSA-Software und intelligente Assistenzsysteme. An diese Arbeiten knüpft die vorliegende Dissertation an und liefert weitere Artefakte, um insbesondere die Selektion, Evaluation und Anwendung datengetriebener Methoden im Bereich der industriellen Instandhaltung zu unterstützen. Insgesamt erstreckt sich die Abhandlung auf vier Artefakte, die in einzelnen Publikationen erarbeitet wurden. Dies umfasst (i) ein umfangreiches Systematisierungsframework zur Beschreibung zentraler Ausprägungen wiederkehrender Datenanalyseprobleme im Bereich der industriellen Instandhaltung, (ii) ein textbasiertes Assistenzsystem, welches ausgehend von natürlichsprachlichen und domänenspezifischen Problembeschreibungen eine geeignete Klasse von Analysemethoden vorschlägt, (iii) ein taxonomisches Evaluationsframework zur systematischen Bewertung von datengetriebenen Methoden unter verschiedenen Rahmenbedingungen sowie (iv) einen neuartigen Lösungsansatz zur Entwicklung von prognostischen Entscheidungsmodellen im Fall von eingeschränkter Informationslage. Die Konstruktion der Artefakte wird durch einzelne Forschungsziele im Rahmen eines systematischen Forschungsdesigns angeleitet. Neben der Darstellung der einzelnen Forschungsbeiträge unter Bezugnahme auf die erzielten Ergebnisse der dazugehörigen Publikationen werden auch die Verbindungen zwischen den entwickelten Artefakten beleuchtet und Zusammenhänge zu angrenzenden Arbeiten hergestellt. Zudem erfolgt eine kritische Reflektion der Ergebnisse hinsichtlich ihrer Verallgemeinerung und Übertragung auf andere Rahmenbedingungen. Dadurch liefert die vorliegende Abhandlung nicht nur einen Beitrag anhand der erzeugten Artefakte, sondern ebnet auch den Weg für fortführende Forschungsarbeiten, wofür eine detaillierte Forschungsagenda erarbeitet wird.:List of Figures List of Tables List of Abbreviations 1 Introduction 1.1 Motivation 1.2 Conceptual Background 1.3 Related Work 1.4 Research Design 1.5 Structure of the Thesis 2 Systematization of the Field 2.1 The Current State of Research 2.2 Systematization Framework 2.3 Exemplary Framework Application 3 Intelligent Assistance System for Automated Method Selection 3.1 Elicitation of Requirements 3.2 Design Principles and Design Features 3.3 Prototypical Instantiation and Evaluation 4 Taxonomic Framework for Method Evaluation 4.1 Survey of Prognostic Solutions 4.2 Taxonomic Evaluation Framework 4.3 Exemplary Framework Application 5 Method Application Under Industrial Conditions 5.1 Conceptualization of a Solution Approach 5.2 Prototypical Implementation and Evaluation 6 Discussion of the Results 6.1 Connections Between Developed Artifacts and Related Work 6.2 Generalization and Transferability of the Results 7 Concluding Remarks Bibliography Appendix I: Implementation Details Appendix II: List of Publications A Publication P1: Focus Area Systematization B Publication P2: Focus Area Method Selection C Publication P3: Focus Area Method Selection D Publication P4: Focus Area Method Evaluation E Publication P5: Focus Area Method Applicatio