Authors:
Marcel van Rooyen
1
and
Simeon J. Simoff
2
Affiliations:
1
University of Technology, Australia
;
2
School of Computing and Mathematics, University of Western Sydney, Australia
Keyword(s):
Business intelligence, data-mining project methodology, data-mining, Knowledge Discovery in Databases (KDD), knowledge management, concept drift, Telco retention-management, technology enablement.
Related
Ontology
Subjects/Areas/Topics:
Business Analytics
;
Communication and Software Technologies and Architectures
;
Data Engineering
;
Data Warehouses and Data Mining
;
e-Business
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Management Information Systems
Abstract:
Businesses are experiencing difficulties with integrating data-mining analytics with decision-making and action. At present, two data-mining methodologies play a central role in enabling data-mining as a process. However, the results of reflecting on the application of these methodologies in real-world business cases against specific criteria indicate that both methodologies provide limited integration with business decision-making and action. In this paper we demonstrate the impact of these limitations on a Telco customer retention management project for a global mobile phone company. We also introduce a data-mining and analytics project methodology with improved business integration – the Strategic Analytics Methodology (SAM). The advantage of the methodology is demonstrated through its application in the same project, and comparison of the results.