A Framework for Organizational Data Analysis and Organizational Data Mining

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Author(s): Bernd Knobloch (University of Bamberg, Germany)
Copyright: 2008
Pages: 20
Source title: Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59904-951-9.ch033

Keywords: Data Mining and Databases / Data Warehousing / Information Science Reference / Library & Information Science

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Abstract

This chapter introduces a framework for organizational data analysis suited for data-driven and hypotheses-driven problems. It shows why knowledge discovery and hypothesis verification are complementary approaches and how they can be chained together. It presents a methodology for organizational data analysis including a comprehensive processing scheme. Employing a plug-in metaphor, data analysis process engineering is introduced as a way to set up data analysis processes based on taxonomies of tasks that have to be performed during data analysis and on the idea of re-using experience from past data analysis projects. The framework aims at increasing the benefits of data mining and other data analysis approaches, by allowing a wider range of business problems to be tackled and by providing the users with structured guidance for planning and running analyses.

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