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Data Driven Knowledge Discovery for Continuous Process Improvement

In: Knowledge Management in Digital Change

Author

Listed:
  • Michael Kohlegger

    (University of Applied Sciences Kufstein)

  • Christian Ploder

    (Management Center Innsbruck)

Abstract

Knowledge is recognized as an organizational resource for business value creation. The work with knowledge—knowledge work—is thus an important part of value-adding processes in organizations. The ability of knowledge workers to analyze complex phenomena, interpret them and develop meaningful actions is one central part of knowledge work. The advancements of digital aids and especially the ability to analyze big amounts of data is a new phenomenon that is increasingly seen in organizations. In this work, we assume that there needs to be an interplay between digital aids and knowledge workers to allow new, deep insights into phenomena and support business value creation. We develop a model that describes how this interplay could look like and critically discuss it using real-world cases. From that, we find that it is crucial (1) separating data-driven and expert-based analysis in knowledge discovery, (2) clearly describing the problem that should be solved by the analysis, (3) understand the particular domain that analysis is applied to, (4) complement data-driven with expert-based analysis and (5) understand the entanglement of analysis and action implementation.

Suggested Citation

  • Michael Kohlegger & Christian Ploder, 2018. "Data Driven Knowledge Discovery for Continuous Process Improvement," Progress in IS, in: Klaus North & Ronald Maier & Oliver Haas (ed.), Knowledge Management in Digital Change, pages 65-81, Springer.
    • Handle: RePEc:spr:prochp:978-3-319-73546-7_4
    DOI: 10.1007/978-3-319-73546-7_4
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