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

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    What Makes a Good Story - The Use and Acceptance of Storytelling in Business Intelligence
    ( 2021-01-05) Ramm, Saskia ; Kopf, Eva-Maria ; Dinter, Barbara ; Hönigsberg, Sarah
    In the age of big data and analytics, the constantly growing complexity of information requires its suitable visualization. As an increasingly popular visualization technique, storytelling supports the successful discovery, presentation, and communication. However, a scientific discussion about the role of storytelling in Business Intelligence (BI) is still missing. Therefore, we consider it as beneficial to investigate this quite young phenomenon and its characteristics in more detail. In the paper we present a morphological box for storytelling in BI based on the results of an extensive literature review. In addition, we were interested to what extent BI users utilize and accept the storytelling concept. We have answered this research question by analyzing the use and acceptance of the storytelling feature in BI tools by adapting the Unified Theory of Acceptance and Use of Technology (UTAUT).
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    Toward big data and analytics governance: redefining structural governance mechanisms
    ( 2021-01-05) Fadler, Martin ; Legner, Christine
    Big Data and Analytics (BDA) enable innovative business models and, simultaneously, increase existing business processes’ efficiency and effectiveness. Although BDA’s potential is widely recognized, companies face a variety of challenges when adopting BDA and endeavoring to generate business value. Researchers and practitioners emphasize the need for effective governance to delineate data and analytics’ roles and responsibilities. Existing studies focus either on data or on analytics governance, even though both approaches are closely interlinked and depend on each other. Our study aims to integrate these two distinct research perspectives into a unified view on structural mechanisms for BDA. Using design science research, we iteratively develop data and analytics roles, clarify their responsibilities and provide guidelines for their organizational assignment. Our study contributes to advancing research on data and analytics governance and supports practitioners managing BDA.
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    Realizing Value with Data and Analytics: A Structured Literature Review on Classification Approaches of Data-Driven Innovations
    ( 2021-01-05) Kayser, Liza ; Fruhwirth, Michael ; Mueller, Roland M.
    Due to the growing importance of data-driven innovation, multiple streams of literature that offer varying definitions and frameworks for using data and analytics in innovation have emerged. This eventually resulted in synonymously used terminology and overlapping concepts leading to a lack of clarity and transparency. This paper investigates different aspects and variations of existing classification approaches, such as taxonomies, around data-driven innovations, and related fields. For this purpose, a systematic literature review was conducted. The resulting 30 publications were synthesized along the concepts type of study objects, type of output investigated as well as type of value dimension influenced by data and analytics. The review underlines the importance of connecting the different literature streams (e.g. data-driven or analytics business model innovation, or Analytics-as-a-Service) which emerged in recent years and hence developing a common language and knowledge basis around data-driven innovation.
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    Pathways of Data-driven Business Model Design and Realization: A Qualitative Research Study
    ( 2021-01-05) Rashed, Faisal ; Drews, Paul
    Maximizing the value from data has become a key challenge for companies as it helps improve operations and decision making, enhances products and services, and ultimately, leads to new business models (BMs). Aiming to achieve the latter, companies take different pathways. Building on a grounded theory research approach, we identified four pathways for designing and realizing data-driven business models (DDBMs). To achieve this goal, we conducted 16 semi-structured interviews with experts from consulting and industry firms. The results fill the gap in the literature on the design and realization of DDBMs and act as a guide for companies.
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    Design of a Process Mining Alignment Method for Building Big Data Analytics Capabilities
    ( 2021-01-05) Pfahlsberger, Lukas ; Mendling, Jan ; Eckhardt, Andreas
    Process mining is a big data analytics technique that supports business process management in an evidence-based way. Nowadays, companies struggle to build the required capabilities that lift process mining beyond technical proof-of-concept implementations. As research on process mining is largely limited to algorithm design and project management recommendations, current research does not understand well how process mining and complementary resources and capabilities can be aligned. By understanding those interrelations, companies learn to leverage their organizational potential during the execution of process mining more effectively and efficiently. In this paper, we address this research gap by using the design science research approach to develop a process mining alignment method. Our method supports companies mapping their individual technical requirements of process mining to their underlying organizational resources. We evaluate our method through a series of interviews with IT consultants.
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    Assembling Algorithmic Decision-Making under Uncertainty: The Case of 'Edge Cases' in an Open Data Environment
    ( 2021-01-05) Grønsund, Tor
    Algorithmic decision-making is rapidly evolving as a source of data-driven competitive advantage with important implications for analytical practices in multiple settings. Despite the ambitions for algorithmic and intelligent technologies, however, the requirement for quality data input to the algorithm poses a significant challenge for its actual adoption. The trend towards open data might bring additional challenges such as strategic gaming and distortion of meaning. To address this problem, we draw on a two-year long qualitative case study of a firm in international maritime trade to understand the role of uncertainty associated with open data upon the uptake of a novel algorithm. We combine an uncertainty and assemblage perspective to unpack the arrangements by which the organization configures relations of humans and machine to mitigate this problem. We highlight the phenomenon of edge cases as a key challenge for automation and propose that an assemblage of augmentation and automation allows a dynamic arrangement that support the introduction and organization of algorithmic decision-making under uncertainty.
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