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On approach for the implementation of data mining to business process optimisation in commercial companies

    Aleksander Pick Affiliation
    ; Olegas Vasilecas Affiliation
    ; Diana Kalibatienė Affiliation
    ; Rok Rupnik Affiliation

Abstract

Nowadays, organisations aim to automate their business processes to improve operational efficiency, reduce costs, improve the quality of customer service and reduce the probability of human error. Business process intelligence aims to apply data warehousing, data analysis and data mining techniques to process execution data, thus enabling the analysis, interpretation, and optimisation of business processes. Data mining approaches are especially effective in helping us to extract insights into customer behaviour, habits, potential needs and desires, credit associated risks, fraudulent transactions and etc. However, the integration of data mining into business processes still requires a lot of coordination and manual adjustment. This paper aims at reducing this effort by reusing successful data mining solutions. We propose an approach for implementation of data mining into a business process. The confirmation of the suggested approach is based on the results achieved in eight commercial companies, covering different industries, such as telecommunications, banking and retail.

Keyword : business process, data mining, CRISP-DM, ontology, SOA

How to Cite
Pick, A., Vasilecas, O., Kalibatienė, D., & Rupnik, R. (2013). On approach for the implementation of data mining to business process optimisation in commercial companies. Technological and Economic Development of Economy, 19(2), 237-256. https://doi.org/10.3846/20294913.2013.796501
Published in Issue
Jun 17, 2013
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