Towards effective Agile Data Science

  • K. (Kevin) de Jong

    Student thesis: Master's Thesis


    Despite emerging possibilities to gain value from Knowledge Discovery, organizations are starting Data Science project that have a little chance of success, as current failure rates show us. Facing several impactful developments, such as the proliferation of Big Data, significant adjustments on traditional methodologies would make sense. Agile has the potential to inspire new artefacts to better connect Data Science activities with nowadays requirements.
    The purpose of this study is to seek for contribution to the shift towards effective Data Science activities by testing the potential of an Agile-inspired design. A design science research was chosen as research method. For evaluating the proposed design, two case studies were conducted.
    This research introduces the DataOps methodology, an agile inspired way of working that helps teams working within the field of Knowledge Discovery improve their results. It can be concluded that the proposed methodology has potential to move current methodologies towards, as is title of this
    research is called, effective Agile Data Science.
    Date of Award10 Jul 2019
    Original languageEnglish
    SupervisorRemko Helms (Examiner) & Jeroen Baijens (Co-assessor)


    • DataOps
    • Design Science Research
    • Data Science
    • Agile,
    • Methodology
    • Knowledge Discovery
    • DevOps

    Master's Degree

    • Master Business Process management & IT (BPMIT)

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