Modeling the strategic objectives has been shown to be useful both for understanding a business as well as planning and guiding the overall activities within an enterprise. Business strategy is modeled according to human expertise, setting up the goals as well as the indicators that monitor activities and goals. However, usually indicators provide high-level aggregated views of data, making it difficult to pinpoint problems within specific sub-areas until they have a significant impact into the aggregated value. By the time these problems become evident, they have already hindered the performance of the organization. However, performing a detailed analysis manually can be a daunting task, due to the size of the data space. In order to solve this problem, we propose a user-driven method to analyze the data related to each business indicator by means of data mining. We illustrate our approach with a real world example based on the Europe 2020 framework. Our approach allows us not only to identify latent problems, but also to highlight deviations from anticipated trends that may represent opportunities and exceptional situations, thereby enabling an organization to take advantage of them.

Monitoring and Diagnosing Indicators for Business Analytics

Zoumpatianos, Konstantinos;Palpanas, Themistoklis;Mylopoulos, Ioannis
2013-01-01

Abstract

Modeling the strategic objectives has been shown to be useful both for understanding a business as well as planning and guiding the overall activities within an enterprise. Business strategy is modeled according to human expertise, setting up the goals as well as the indicators that monitor activities and goals. However, usually indicators provide high-level aggregated views of data, making it difficult to pinpoint problems within specific sub-areas until they have a significant impact into the aggregated value. By the time these problems become evident, they have already hindered the performance of the organization. However, performing a detailed analysis manually can be a daunting task, due to the size of the data space. In order to solve this problem, we propose a user-driven method to analyze the data related to each business indicator by means of data mining. We illustrate our approach with a real world example based on the Europe 2020 framework. Our approach allows us not only to identify latent problems, but also to highlight deviations from anticipated trends that may represent opportunities and exceptional situations, thereby enabling an organization to take advantage of them.
2013
Proceedings of the 2013 Conference of the Center for Advanced Studies on Collaborative Research
a VV.
USA
IBM Corporation
Zoumpatianos, Konstantinos; A., Maté A; Palpanas, Themistoklis; J., Trujillo; Mylopoulos, Ioannis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/66701
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