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

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Now showing 1 - 7 of 7
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    Interconnectivity of Big Data Sources: Exploring Synergistic Relationships among Data
    ( 2019-01-08) Weibl, Johannes
    In recent years, organizations manage an increasing amount of data in order to make better decisions, personalize products, or sell data. By data being combined from various sources, data assets interact with each other. When the interactions are synergistic, they create greater benefits than the sum of the value of the individual data assets. This study explores enablers, mechanisms, and potential outcomes of synergistic interactions among data assets. Based on systems theory and a synthesis of relevant synergy literature, I developed an initial synergy framework in a data context. On this basis, I conducted 14 qualitative interviews to assess the validity of my initial framework. The interview results assisted me in refining and contextualizing a unified conceptual framework of data synergies. The paper reveals that compatibility and contextual relatedness as enablers and informational complementary as a mechanism can lead to super-additive information value among data assets.
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    Business Intelligence System Adoption, Utilization and Success - A Systematic Literature Review
    ( 2019-01-08) Ul-Ain, Noor ; Vaia, Giovanni ; DeLone, William
    In recent era of technological advances and hyper-competition, Business intelligence (BI) systems have attracted significant attention from executives and decision makers, due to their ability to provide complex and competitive information inputs to the decision process. Research into the adoption, utilization, and success of BI systems has grown substantially over the past two decades. Evidence from the existing literature suggests that organizations have largely failed to capture the full benefits of BI systems. This study uses a systematic literature review to present comprehensive knowledge about what has been examined in the domain of BI system adoption, utilization, and success. The study reports that although user under-utilization and resistance are key challenges, little empirical research has focused on user-centered issues.
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    Understanding User Uncertainty during the Implementation of Self-Service Business Intelligence: A Thematic Analysis
    ( 2019-01-08) Weiler, Severin ; Matt, Christian ; Hess, Thomas
    Owing to Self-Service Business Intelligence (SSBI) systems’ transformative power for organizations, substantial user uncertainties often blight their potential. Although these uncertainties pose a significant threat to effective SSBI implementation, their sources and determinants remain unclear. We conducted semi-structured interviews with 15 current users of a recently implemented SSBI system to empirically explore the relevant factors of user uncertainty. We undertook a rigorous thematic analysis of the collected data, thereafter developing a thematic map to visualize user uncertainties. This map uncovered three unexplored important factors (work routine change, social dynamics and fear of AI) for future research. Our findings show that users are not only perturbed by “hard” factors (e.g. a lack of technical understanding), but also by “soft” factors (social dynamics, fear of AI and nontransparency). Practitioners can use the thematic map to identify and observe potential uncertainties and to develop adequate procedures.
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    Analytics Use Cases for Mass Customization – A Process-based Approach for Systematic Discovery
    ( 2019-01-08) Wache, Hendrik ; Dinter, Barbara ; Kollwitz, Christoph
    Nowadays, mass customization (MC) is shaped by the application of digital technologies like computer-aided design, computer aided manufacturing, and distribution planning. Within a MC process, various data is created, which can be used to gain knowledge about past and future business activities by means of modern data analytics methods. The paper at hand applies design science research and presents a process-based approach for identifying potential analytics use cases for MC. For this purpose, a generic MC process is derived from previous literature and a systematic analysis is carried out using the work systems method. The resulting artifact offers a differentiated view on customers, products, activities, participants, technologies, and information as well as on the information flows within the MC process. It enables manufacturers to identify valuable opportunities for analytics and to optimize current MC processes. Furthermore, it can be used to develop a systematic process for the discovery and evaluation of analytics use cases and novel business models in the future.
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    Data Philanthropy: An Explorative Study
    ( 2019-01-08) George, Jordana ; Yan, Jie (Kevin) ; Leidner, Dorothy
    Data philanthropy, which is firm donations of data, data scientists, and data technologies for social good, is a powerful new phenomenon that offers benefits to both donor firms and society. In this explorative research, we unpack data philanthropy, providing definitions, and examples along with a theoretical perspective from corporate philanthropy and strategic management. We view data through a lens from the resource-based view of the firm. Based on the premise that data is an asset of the firm, we discuss how data philanthropy conforms and differs from traditional corporate philanthropy. Given data’s requirements for substantial complementary assets and appropriate context, we propose that data can be shared for social good without harming the firm and may result in unforeseen benefits for the firm. In analyzing three examples, we offer several propositions regarding this new phenomenon.
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    Configuring The Internet of Things (IoT): A Review and Implications for Big Data Analytics
    ( 2019-01-08) Williams, Susan ; Hardy, Catherine ; Nitschke, Patrick
    Big data analytics is emerging as a key initiative in the IoT field as data grows at unprecedented scale and depth. However, considerable uncertainty remains about how organizations are using big data analytics to capitalize on IoT. In this paper we argue that there is a need for a more refined depiction of the relationship between IoT and big data analytics as it tends to be linked by technological and economic viewpoints. Three principal claims are made. Firstly, there is a pressing need to clarify the characteristics configuring and shaping the discourses around IoT. We find that IoT is characterized as a complex, (more than) technological, multi-scale and multi-level information infrastructure that is emergent and uncertain. Secondly, the unique characteristics of IoT are challenging governance capabilities in big data analytics. Thirdly, the impact of IoT through big data analytics for building ‘sustainable futures’ raises questions about responsible research and innovation.
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