Business Intelligence System

Topics: Data mining, Business intelligence, Data warehouse Pages: 23 (5458 words) Published: January 21, 2013
Interdisciplinary Journal of Information, Knowledge, and Management

Volume 1, 2006

Business Intelligence Systems
in the Holistic Infrastructure Development
Supporting Decision-Making in Organisations
Celina M. Olszak and Ewa Ziemba
University of Economics, Katowice, Poland

The paper aims at analysing Business Intelligence Systems (BI) in the context of opportunities for improving decision-making in a contemporary organisation. The authors – taking specifics of a decision-making process together with heterogeneity and dispersion of information sources into consideration – present Business Intelligence Systems as some holistic infrastructure of decisionmaking. It has been shown that the BI concept may contribute towards improving quality of decision-making in any organisation, better customer service and some increase in customers’ loyalty. The paper is focused on three fundamental components of the BI systems, i.e. key information technologies (including ETL tools and data warehouses), potential of key information technologies (OLAP techniques and data mining) and BI applications that support making different decisions in an organisation. A major part of the paper is devoted to discussing basic business analyses that are not only offered by the BI systems but also applied frequently in business practice. Keywords: Business Intelligence, data mining, OLAP, ETL, business decision-making, knowledge management

Business Intelligence Systems in Decision-Making
Decision-making in management has always involved utilisation of different information assets. Contemporary economic conditions show that organisations are more frequently made to use external, dispersed and semi-structured sources of information. In today’s decision-making, it is necessary to reach for information. However, it is knowledge that has to be mainly looked for. Knowledge provides foundations for effective business activities. Procedural knowledge (explaining how to perform tasks and follow procedures) should be accompanied by declarative knowledge (indicating what has to be done), semantic knowledge (depicting relations between facts) and casuistic knowledge (that refers to some cases from the past). So-called tacit knowledge is a large part of knowledge in an organisation. Organisations that are interested to use knowledge in

decision-making are forced to work out
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knowledge. In this situation, organisaprovided that the copies are not made or distributed for profit tions find it necessary to create repositoor commercial advantage AND that copies 1) bear this notice ries of knowledge and knowledge manin full and 2) give the full citation on the first page. It is permissible to abstract these works so long as credit is given. To agement systems, simultaneously findcopy in all other cases or to republish or to post on a server or ing the way to match them with decision

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Editor: Alex Koohang

Business Intelligence Systems

Nowadays, different groups of people participate in decision-making (stakeholders, customers, suppliers, etc.). The scope of a particular decision is in many cases of global nature. Regional and international interdependencies require wider exchange of information and knowledge sharing, and better coordination of activities undertaken in contrast to everything that took place in the past (Viehland, 2005).

Dispersion of information assets and their frequently tacit nature results in...

References: Bui, T. (2000). Decision support systems for sustainable development. In G. E. Kersten, Z. Mikolajuk, &
Gray, P., & Watson, H. (1998). Decision support in the data warehouse. Prentice Hall.
Gray, P. (2003). Business intelligence: A new name or the future of DSS. In T. Bui, H. Sroka, S. Stanek, &
Hackathorn, R. D. (1998). Web farming for the data warehouse. Morgan Kaufmann.
Hauke, K., Owoc, M. L., & Pondel, M. (2003). Building data mining models in the Oracle 9i environment.
Proceedings of Informing Science and IT Education, 2003. Santa Rosa: The Informing Science Institute. Retrieved December 1, 2005, from
Inmon ,W. H. (1992). Building the data warehouse. New York: J. Wiley.
Kalakota, R. & Robinson, M. (1999). E-business: roadmap for success. Addison-Wesley.
Kantardzic, M. (2002). Data mining: Concepts, models, methods and algorithms. New York: J. Wiley.
Kersten, G. E. (2000). Decision making and decision support. In G. E. Kersten, Z. Mikolajuk, & A. Gar-on
Yeh (Eds.), Decision support systems for sustainable development
Liautaud, B., & Hammond, M. (2002). E-business intelligence. Turning information into knowledge into
Linoff, G. S., & Berry, M. J. A. (2002). Mining the web: transforming customer data into customer value.
Meyer, S. R. (2001, June). Which ETL tool is right for you?. DM Review Magazine.
Moss, L. T. & Alert, S. (2003). Business intelligence roadmap – The complete project lifecycle for decision
support applications
Olszak, C. M., & Ziemba, E. (2003). Business intelligence as a key to management of an enterprise. Proceedings of Informing Science and IT Education, 2003. Santa Rosa: The Informing Science Institute.
Retrieved December 1, 2005, from
Olszak, C. M., & Ziemba, E. (2004). Business intelligence systems as a new generation of decision support
Poul, S., Gautman, N., & Balint, R. (2003). Preparing and data mining with Microsoft SQL Server 2000
and Analysis Services
Rasmussen, N., Goldy, P. S., & Solli, P. O. (2002). Financial business intelligence. Trends, technology,
software selection, and implementation
Reinschmidt, J., & Francoise, A. (2000). Business intelligence certification quide. IBM, International
Technical Support Organization.
Silva, R., & Rahimi, I. (2004). Issues in implementing CRM: Acase study. Journal of Issues in Informing
Science and Information Technology 2004(1)
December 1, 2005, from
Thuraisingham, B. (2003). Web data mining and applications in business intelligence and counterterrorism. Auerbach Publications.
Turban, E., & Aronson, J. E. (1998). Decision support systems and intelligent systems. Prentice Hall.
Viehland, D. (2005). ISExpertNet: Fcilitating knowledge sharing in the information systems academic
Science Institute. Retrieved October 12, 2005, from
Wells, J. D., & Hess, T. J. (2004). Understanding decision-making in data warehousing and related decision
support systems
Wijnhoven, F. (2001). Models of information markets: Analysis of markets, identification of services, and
design models
Rosa: The Informing Science Institute. Retrieved October 1, 2005, from
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