On Data Mining and Knowledge: Questions of Validity

On Data Mining and Knowledge: Questions of Validity

Oliver Krone (Independent Scholar, Germany)
DOI: 10.4018/978-1-60960-783-8.ch115

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Introduction

The purpose of this paper is examining what grants the special status of data mining (DM), and more general business intelligence (BI), in the dedicated organisation and in organisational sciences. On a conceptional level DM is understood as part of Information systems (IS) research. Furthermore, this paper is interested learning and developing an account of DM and its knowledge creation capabilities. The scope of this paper is limited to intra-organisational and academic development and spread of knowledge to create better “action options”. It does not consider actual methods for data mining. The paper is not reporting about DM procedures for generation of data pools originating in different organisations. The author examines the processes of ‘knowledge’ generation and validity assignment; the paper is of theoretical nature, and rests on interpretative methods.

Two research questions are examined

How does data mining fit into organizations’ information system landscape for information (and knowledge) collection and spread?

How validity is attributed to knowledge that is generated by DM, while other methodological approaches are neglected in organisations and organisational sciences?

Based on these research questions in an introduction chapter an overview to information systems (IS) and how DM is related is given. Following this introduction, knowledge/ Knowledge Management and methods for knowledge generation are examined based on an Penrose’an understanding of knowledge. Penrose is chosen here, as she is widely perceived as one “founder” of knowledge management. Taking up the question why DM based knowledge is more valued then qualitative methods originating one, characteristics of knowledge formation in both methodological stances are presented. It is examined how DM relates to criticism brought forward against knowledge generated outside of academic and research (e.g. Ravetz 1996; Thompson-Klein 1996; Nowotny et al., 2004). Arguments developed there, form the background for identification of Future Trends in knowledge generation via DM. The chapter concludes in consolidating the technical background of DM under the heading of knowledge management and the alleged methodological superiority of DM. This consolidation happens based on Foucault’s analysis of security and how IS relate to this understanding.

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