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  • Effective big data management : a development of critical success factors and an analysis of firms' capabilities in the automotive industry
    Publication . Nieder, Nicolai; Grando, Alberto
    This dissertation examines the analytical capabilities of firms with regard to effective big data management in the commercial vehicle industry. Value creation through effective big data management has been and will be one of the key challenges for organizations in data-generating industries such as the commercial vehicle one. Hence, a model with Critical Success Factors (CSF) for analytical capabilities is developed from theory. The model, with each seven CSF grouped into strategic/organizational, human, and technological capabilities, is then validated through two separate interviews with leading IT and industry experts. After validation, a survey is designed around the CSF developed in order to gain insights into the main players of the industry. The survey is distributed by a German-based consultancy (partner of this dissertation) to its automotive clients’ IT and sales/marketing departments, representing a major part of the industry. The survey results show scores around the mean with regard to all three dimensions of analytical capabilities with the strategic/organizational dimension at 4.25, human at 3.79 and technological at 3.91 (scores between 1 and 7). Additionally, IT and business managers differ in their perception of the own organization’s capabilities, with the most significant difference concerning the human capabilities. All in all, IT managers’ scores are lower than the business managers ones along all three dimensions of the model. In future, the developed CSF model can serve as the foundation for an initial self-assessment for companies in data-generating industries with regard to analytical capabilities before conducting further more detailed analyses.
    2016-02-22Dissertação de mestrado Acesso restrito Ver mais