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The use of analytical tools and disruptive technologies is a strategic imperative for companies to operate successfully in global markets. Wilo, a leading premium provider of pumps and pump systems, tasked Holger Jentsch, the Vice President (VP) of the Group Sales Excellence department, with piloting initial technological transformation projects in certain sales processes. As a lighthouse project, Jentsch aimed to use an artificial intelligence (AI) based analytics tool to prevent customer churn. Therefore, the case outlines critical success factors for the implementation of data-based decision-making which are elementary for Jentsch’s digital transformation project.
This publication-based dissertation examines human-related success factors for the implementation and application of data analytics tools and methods within the decision-making process of organizations. Generated insights on human-related factors are outlined and described in six chapters. First, a general introduction to the subject is provided and the research is positioned within a broader overall context. Additionally, the first section comprises a summary of the research papers included, along with publication information. Chapter 2 presents a systematic literature review summarizing the capabilities of Big Data analytics (BDA) with regard to firm performance. Five key capability clusters have been identified to categorize all relevant human-related capabilities across existing research to date. Chapter 3 presents an empirical research paper examining the relevant managerial aspects that must be considered when shifting from intuitive to analytics-based decision-making. Introducing a six-factor framework, the chapter outlines the findings of an indepth single case study of a German manufacturing organization that has already implemented analytical methods and tools within its decision processes. Chapter 4 contains the second empirical paper, which outlines the crucial role that executives play within the process of a firm’s digital transformation toward the application of analytics. Based on conducted interviews, four managerial archetypes are identified, with detailed descriptions of their characteristics, capabilities, and contribution to transformation. Chapter 5 introduces a teaching case study that sheds light on best practices relevant to the application of analytics. This case study describes the most critical factors for success in the use of an AI tool using an example from Wilo, a leading German manufacturer of pumps and pump systems. Finally, Chapter 6 summarizes the findings of this publication-based dissertation, outlines its contributions to academia and practice, and presents its limitations and potential avenues for future research.
Research shows that many firms still make business critical decisions intuitively, despite clear evidence that analytics-based decision-making is likely more effective in creating corporate and social value. With the aim of providing actionable guidance to firms on how to accomplish the shift to analytics-based decision-making, this paper sheds light on the management factors that prove critical in this context. An in-depth single-site case study was conducted with a large publicly listed German manufacturing company. Building on 22 semi-structured interviews, this empirical study identifies six factors that play a critical role in establishing analytics-based decision-making: management behaviour, top management and strategy, analytics infrastructure, organisation and governance, HR management and development, and culture. This study forms the basis for further scientific research on the role of firm management in the transitional phase. Furthermore, it provides firm leaders with a systemised and practical framework to structure firm efforts to establish data-based decision making.
The use of analytics in corporate decision-making processes demands a paradigm shift within companies, and particularly among their top executives. Corporate leaders represent a major lever for this change. Therefore, a deeper understanding of their managerial capabilities, characteristics and contribution in this context is required. Aiming to provide actionable guidance on how to manage the shift to data-driven decision making, this study helps to develop a more profound understanding of this emerging managerial role by examining managerial success factors following a semi-structured interview approach. With insights from interviews with 32 top executives from Germany across different industries, this paper research proposes four managerial archetypes that are relevant to mastering the digital transformation towards analytics-based decision-making processes. Furthermore, it sheds light on the characteristics, capabilities, and contributions of the four archetypes—Analytical Thinker, Coach, Guide, and Strategist. Although the archetypes have differentiated attributes and qualities, all four seem of importance in manifesting analytics in organizations. Our findings provide guidelines to assess the top management's abilities to manage digital transformation projects. Furthermore, the results serve as basis for future empirical research on the human aspect of analytical capabilities regarding leadership.
This study intends to provide scholars and practitioners with an understanding of human resource challenges in the context of Big Data Analytics (BDA). This paper provides a holistic framework of human-related capabilities that organizations must consider when implementing BDA to facilitate decision-making. For this purpose, the authors conducted a systematic literature review adapted from Tranfield et al. (BJM 14:207–222, 2003) to identify relevant studies. The 75 publications reviewed provided the sample for an inductive, and systematic data evaluation following the well-known and accepted approach introduced by Gioia et al. (ORM 16:15–31, 2012). The comprehensive review uncovered 33 first-order concepts linked to human-related capabilities, which were distilled into 15 s-order themes and then merged into five aggregated dimensions: Personnel Capability, Management Capability, Organizational Capability, Culture and Governance Capability, and Strategy and Planning Capability. The study is, to the best of the authors’ knowledge, the first to categorize all relevant human-related capabilities for successful BDA application. As such, it not only provides the scientific basis for further research, but also serves as a useful overview of the critical factors for BDA use in decision-making processes.