On the Way from a Knowledge Discovery in Databases to a Predictive Analytics

Claudia Koschtial () and Carsten Felden ()
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Claudia Koschtial: Technische Universität Bergakademie Freiberg
Carsten Felden: Technische Universität Bergakademie Freiberg

A chapter in Business Intelligence for New-Generation Managers, 2015, pp 17-26 from Springer

Abstract: Abstract Business Intelligence has “decision support” as a characterizing element. Decisions are done as a selection process based on alternatives. The choice depends on prospective developments whereby those developments are predicted with uncertainty. Due to this reason, forecasts are getting more into focus of the strategic and tactical level. But forecasts, usually based on Knowledge Discovery in Databases (KDD), are limited, yet. They often produce non-adequate results, which can lead to wrong decisions. Such a forecast quality demands further research in identifying improvements to increase reliability of forecast results and its usage in practice. This chapter modifies the Knowledge Discovery in Databases to improve the forecast quality. The associated process is supplemented by further steps to enhance the analyzed data set with additional future oriented data by using the KDD markup language. First results of an evaluation implementation at a German saving and loans bank shows motivating results.

Keywords: Business Intelligence; Knowledge Discovery in Databases; Forecast; Data Mining; Predictive Analytics (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-15696-5_2

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DOI: 10.1007/978-3-319-15696-5_2

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