Business Intelligence, Business Analytics and Big Data: Innovation, Deployment, and Management
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ItemUnravelling the relationship between a firm’s big data analytics capability and the realization of a competitive advantage: an IT business value approach( 2022-01-04)Big Data Analytics (BDA) has the potential to transform business models, firms and the competitive landscape. Though, creating value from BDA investments seems challenging as many technical and managerial challenges are involved. Due to its complexity, the value generated from big data depends on how well a firm’s Big Data Analytics Capability (BDAC) is developed. Drawing on the Resource Based View (RBV), the IT business value approach and the BDAC literature, we study the relationship between a firm’s BDAC and the realization of a competitive advantage. We used survey data from multiple respondents per firm (i.e. IT managers and Business managers) in 112 Belgian and Dutch firms. Using PLS-SEM, we found a direct relationship between a firm’s BDAC and the perceived realization of a competitive advantage. We also found a partial mediation of this relationship via the performance of the firm’s operation management process.
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ItemTowards Bridging the Gap Between BDA Challenges and BDA Capability: A Conceptual Synthesis Based on a Systematic Literature Review( 2022-01-04)Big data analytics (BDA) and strategies for implementing BDA have received attention among researchers and practitioners alike. However, success stories pertaining to the implementation of BDA remain scarce. The notion of the BDA deployment gap describes the chasm between the attributed value potential of BDA and its actual value realization in organizational practice. Several research articles indicate challenges encountered in implementing BDA but lack a comprehensive systematization of BDA implementation-related challenges. This research article aims to systematize those challenges through a systematic literature review. As a result, we derived five overarching challenge dimensions related to the BDA implementation. Based on this systematization, we adopt the lens of a big data analytics capability and delineate future research avenues through the derivation of propositions on how to overcome the BDA implementation-related challenges, while enhancing our understanding about how to solve the BDA deployment gap.
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ItemThe O-Factor: Aligning Organizational Arrangements with Big Data Analytics Diffusion( 2022-01-04)This study aims to better understand how and why organizational arrangements of Big Data Analytics (BDA) evolve over time in established firms. As BDA initiatives grow in scope and importance, organizational arrangements tend to change, with changes impacting the success of the initiative. This study focuses on the importance of four constructs influencing organizational arrangements during BDA diffusion: the analytics structure, the leadership role, the culture, and the employee skills. Propositions derived from the literature guide the analysis of seven case studies of organizations adopting BDA. The findings help to understand BDA diffusion through (1) aligning structure with business value creation, (2) (new) leadership that trusts and shows exemplary usage of BDA, (3) a culture of trust with constant experimentation for business opportunities and (4) more diversified employee roles. A discussion of academic and managerial implications and suggestions for future research completes this study.
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ItemRequirements for Data Valuation Methods( 2022-01-04)Data is considered the most significant intangible asset for the 21st century enterprise. Serving as key asset for ever-increasing digital transformation and entrepreneurship, they ensure economic success through empowering new technologies, services and business models. Despite their high relevance, there exist neither consistent valuation methods nor specific requirements for developing such methods. Data valuation is crucial in order to better understand their value and, for example, incorporating them into financial statements. Existing literature indicates relationship between data value and quality. Thereupon, we conducted semi-structured expert interviews to gain insights on data valuation methods in connection with data quality. This results in 11 requirements for data valuation methods and seven value-driving quality criteria. Furthermore, several challenges for future data valuation are derived from the empirical results.
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ItemLooking Ahead: Business Intelligence & Analytics Research in the Post-Pandemic New Normal( 2022-01-04)The COVID 19 black swan event has disrupted every aspect of life in unprecedented ways, causing organizations to scramble to effectively sense and respond to the tumultuous business environment. Business intelligence and analytics (BI&A) capability has gained attention as a key weapon in the arsenal needed to combat turbulent times and to adjust to the post-pandemic new normal. Post-pandemic BI&A trends point to changes in organizational priorities for BI&A infrastructure that influence the traditional view of BI&A architecture and its role within an organization. As a result, new challenges and opportunities are emerging. This paper identifies and examines twelve key post-pandemic BI&A trends from industry practice and six major research themes. It also proposes an initial set of research questions that could inspire future research in BI&A in the post-pandemic new normal.
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ItemInstitutionalizing Analytic Data Sharing in SME Ecosystems – A Role-Based Perspective( 2022-01-04)There is a variety of reasons that sharing data among Small and Medium-Sized Enterprises (SMEs) carries business potential, particularly for analyti-cal applications. But outside a few niche domains, the number of success stories for data sharing is rather modest. Based on a qualitative study and first experiences from a research project with pilot im-plementations, we argue that this is mainly due to a lack of an institutionalized governance structure: Founding a separate legal entity for data sharing and analysis can address core concerns regarding sharing valuable data assets. However, this requires a well-calibrated set of defined roles for the in-volved partners. Based on our results we propose a first concept on delineating and mapping out those roles.
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ItemFacing Big Data System Architecture Deployments: Towards an Automated Approach Using Container Technologies for Rapid Prototyping( 2022-01-04)Within the last decade, big data became a promising trend for many application areas, offering immense potential and a competitive edge for various organizations. As the technical foundation for most of today´s data-intensive projects, not only corresponding infrastructures and facilities but also the appropriate knowledge is required. Currently, several projects and services exist that not only allow enterprises to utilize but also to deploy related technologies and systems. However, at the same time, the use of these is accompanied by various challenges that may result in huge monetary expenditures, a lack of modifiability, or a risk of vendor lock-ins. To overcome these shortcomings, in the contribution at hand, modern container and task automation technologies are used to wrap complex big data technologies into re-usable and portable resources. Those are subsequently incorporated in a framework to automate the deployment of big data architectures in private and limited resources.
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ItemData Swagger: A Systemic Approach to Train, Motivate and Engage Data Savvy Employees( 2022-01-04)The relevance of data literacy has increased substantially over the past three decades. When trained well, data-literate employees at all levels can make data-driven decisions, improving the overall performance of their organization. Utilizing Transformative Learning Theory (TLT) and Experiential Learning Theory (ELT), this paper proposes a systematic data education framework for increasing data literacy across organizations. Focusing on the needs and experiences of non-expert end-users, this model proposes the following four learning strategies in data literacy training design: experiential data training, critical incident reflection, rational open discourse, and autonomous experimentation. To inform this model and further investigate barriers to data literacy in organizations, interviews were conducted with individuals from two different data analytics units in the U.S. Department of Defense. This research provides key insight and practical suggestions for developing and improving data literacy training programs.
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ItemA Reference Process Model for Usage Data-Driven Product Planning( 2022-01-04)Cyber-physical systems generate and collect huge amounts of usage data during operation. Analyzing these data may enable manufacturing companies to identify weaknesses and learn about the users of their products. Such insights are valuable in the early phases of product development like product planning, as they facilitate decision-making for product improvement. The analysis and exploitation of usage data in product planning, however, is a new task for manufacturing companies. To reduce mistakes and improve the results, companies should build upon a suitable reference process model. Unfortunately, established models for analyzing data cannot be easily applied for product planning. In this paper, we propose a reference process model for usage data-driven product planning. It builds on three well-established models for analyzing data and addresses the unique characteristics of usage data-driven product planning. Finally, we customize the model for a manufacturing company and demonstrate how it could be implemented in practice.
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