Business Intelligence, Business Analytics and Big Data: Innovation, Deployment, and Management

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https://hdl.handle.net/10125/63705

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    Understanding Challenges and Success Factors in Creating a Data-Driven Culture
    ( 2020-01-07) Storm, Myrthe ; Borgman, Hans
    Increasingly, organizations aspire to practices of data-driven decision making. The necessary transformation to a data-driven culture poses challenges, and this paper explores these as well as success factors. The study is based on six in-depth case studies of organizations that are in different phases of their transformation towards a data-driven organization. Propositions derived from change management and digital transformation literature guide our exploration. Our findings show how challenges and responses differ across the various stages of the transformation. Challenges include resistance to new technology; rigid organizational structures; and too little focus on usable analyses. Success factors include clear communication and leading by example by top-management; showing relevant and clear results of the transformation; and openness to experimentation. A discussion of implications and future research directions rounds off the paper.
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    Mastering Omni-Channel Retailing Challenges with Industry 4.0 Concepts
    ( 2020-01-07) Janhofer, Dustin ; Barann, Benjamin ; Cordes, Ann-Kristin ; Becker, Jörg
    Omni-Channel Management is an important trend, which allows retailers to improve customer experiences. Notwithstanding, entirely seamless integration of all channels, for example, in terms of customer or pricing data or consistent product offerings, is still a challenging endeavor. Technological developments, such as Industry 4.0 (I4.0), lead to innovation opportunities in the production industry. As there are intersections between I4.0 and Omni-Channel retailing, we propose that prominent Omni-Channel retailing challenges can be overcome by integrating knowledge from both research domains. Therefore, the purpose of this article is to investigate, which I4.0 concepts are utilized in scientific literature to overcome challenges and how these concepts can be transferred to Omni-Channel Management. To make this knowledge tangible for retailers, this article deduces opportunities on the application of I4.0 concepts in Omni-Channel retailing. The results show that especially IoT networks offer numerous deployment options and even Cyber-Physical Systems and Smart Factories provide related potentials.
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    Data-driven Business Models in Logistics: A Taxonomy of Optimization and Visibility Services
    ( 2020-01-07) Möller, Frederik ; Stachon, Maleen ; Hoffmann, Christina ; Bauhaus, Henrik ; Otto, Boris
    The nature of business conduct is changing due to emerging digital technologies and the ever-increasing role of data as a critical resource. Traditional industry branches such as logistics need to adapt accordingly to keep up with change through digitization and to design adequate business models using data. The present article focuses on investigating the anatomy of these data-driven business models in the logistics sector. In order to achieve this goal, the study develops a taxonomy of data-driven business models in logistics. Start-ups serve as the frame of reference, as they are particularly suitable for deriving explicitly novel and vital business models. The study focuses on two particular types of data-driven business models, namely those offering visibility or optimization services in logistics. The goal of the taxonomy is to uncover the structural composition of such business models and to make the results usable as a morphology for innovation
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    Visualizing Social Documents as Traces of Collaborative Activity in Enterprise Collaboration Platforms
    ( 2020-01-07) Mosen, Julian ; Williams, Susan ; Schubert, Petra
    Enterprise collaboration platforms are large-scale information infrastructures that provide a wide range of tools and functionality to support collaborative work in organizations. These collaborative activities leave digital traces in the form of social documents, which can be analyzed to understand how employees work together to coordinate their joint work. In this paper, we present the findings of a research project to visualize the structure of social documents to prepare them for analysis as traces of collaborative activity. Using the representation of social documents defined in the Social Document Ontology (SocDOnt), we draw on concepts from graph theory to develop a method for the graphical visualization of social documents. Applying this method to analyze the social documents in an operational enterprise collaboration platform, we identify and display different types of social document and define their characteristic structure. Our findings provide the necessary foundation for conducting computational ethnographies of collaborative work.
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    Patterns of Resource Integration in the Self-service Approach to Business Analytics
    ( 2020-01-07) Bani-Hani, Imad ; Tona, Olgerta ; Carlsson, Sven
    The main premise of Self-Service Business Analytics (SSBA) is to make business users autonomous during the data analytical process. To empower business employees, organisations are decentralising their analytical capabilities therefore adopting an SSBA approach. Yet, little is known about how employees integrate resources, such as personal competencies, environment resources including technology, and other employees’ competencies, to generate insights in SSBA. Based on the empirical data of a major Norwegian online marketplace and drawing on service-dominant logic as an analytical framework, we identify and explain two types of resource integration in an SSBA environment: direct and clustered recourse integration (including 1st tier and 2nd tier) enabled and controlled by three types of institutions. We finally discuss some organisational implications and the meaning of each sub-type of clustered resource integration.
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    A Capability Approach for Designing Business Intelligence and Analytics Architectures
    ( 2020-01-07) Ereth, Julian ; Baars, Henning
    Business Intelligence and Analytics (BIA) is subject to an ongoing transformation, both on the technology and the business side. Given the lack of ready-to-use blueprints for the plethora of novel solutions and the ever-increasing variety of available concepts and tools, there is a need for conceptual support for architecture design decisions. After conducting a series of interviews to explore the relevance and direction of an architectural decision support concept, we propose a capability schema that involves actions, expected outcomes, and environmental limitations to identify fitting architecture designs. The applicability of the approach was evaluated with two cases. The results show that the derived framework can support the systematic development of fundamental architecture requirements. The work contributes to research by illustrating how to capture the elusive capability concept and showing its relation to BIA architectures. For further generalization, we created an open online repository to collect BIA capabilities and architectural designs.
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