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  • 1.
    Berndtsson, Mikael
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Lennerholt, Christian
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Svahn, Thomas
    Advectas AB, Malmö, Sweden.
    Larsson, Peter
    Advectas AB, Malmö, Sweden.
    13 Organizations' Attempts to Become Data-Driven2020In: International Journal of Business Intelligence Research, ISSN 1947-3591, E-ISSN 1947-3605, Vol. 11, no 1, p. 1-21Article in journal (Refereed)
    Abstract [en]

    Becoming a data-driven organization is a vision for several organizations. It has been frequently mentioned in the literature that data-driven organizations are likely to be more successful than organizations that mostly make decisions on gut feeling. However, few organizations make a successful shift to become data-driven, due to a number of different types of barriers. This article investigates, the initial journey to become a data-driven organization for 13 organizations. Data has been collected via documents and interviews, and then analyzed with respect to: i) how they scaled up the usage of analytics to become data-driven; ii) strategies developed; iii) barriers encountered; and iv) usage of an overall change process. The findings are that most organizations start their journey via a pilot project, take shortcuts when developing strategies, encounter previously reported top barriers, and do not use an overall change management process.

  • 2.
    Gudfinnsson, Kristens
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Rose, Jeremy
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Aggestam, Lena
    Department of Economics and Informatics, University West, Trollhättan, Sweden.
    Tackling Lack of Motivation in Aspirational Analytics Companies: SME Examples from the Manufacturing Industry2019In: International Journal of Business Intelligence Research, ISSN 1947-3591, E-ISSN 1947-3605, Vol. 10, no 1Article in journal (Refereed)
    Abstract [en]

    Establishing business intelligence analytics (BIA) in small- and medium-sized manufacturing enterprises is a pervasive problem. SME’s - the majority of businesses - play an important role in creating jobs, but research is primarily focused on large corporations. The authors worked with small manufacturing companies at the aspirational capability level but found that their motivation to introduce BIA was low. They had many business challenges but perceived the obstacles (primarily cost and effort) as too great, and their priorities were with operational issues. A two-phase approach based on a well-known analytics maturity model was devised to help raise company motivation. The article describes three studies in different companies using variations of the approach. Comparative analysis of the cases shows that demonstrating a clear path to improved functional efficiency is key to improving motivation, and that simple, easy to learn tools can provide these insights at little cost.

  • 3.
    Strand, Mattias
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Incorporating external data into a BI solution at a public waste management organization2019In: International Journal of Business Intelligence Research, ISSN 1947-3591, E-ISSN 1947-3605, Vol. 10, no 2, p. 36-56Article in journal (Refereed)
    Abstract [en]

    Organizations are showing an increasing interest in incorporating external data into their business intelligence solutions. Such data allows for advanced analytics and enables more comprehensive and inclusive decision-making. However, external data incorporation is relatively unexplored in the literature, and scientifically published details on up-and-running BI solutions are very sparse. In addition, published literature concerning the incorporation of external data into BI solutions is often rather synoptic or rather old (originating from data warehouse related literature). Therefore, the authors present the results of an action case study at a public waste management organization, illustrating detailed aspects of external data incorporation related to the back-end of the solution such as data selection, source characteristics, acquisition technologies and frequencies, and integration approaches. Given that the external origin of the data poses specific problems that must be overcome in order to allow for successful incorporation initiatives, special attention was paid to such problems. Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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