• Knowledge Management Research
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• v.14 no.5
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• pp.137-149
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• 2013

Although business Intelligence system is introduced to many companies over the past decade, The result of business benefits from BI investment are not so significant than expected. But still successful BI system can provide the ability to analyse business information in order to support and improve management decision making across a broad range of business activities. In recently, Business Analytics System(BA) is emerging as advanced alternative of outdated and inefficient BI System. This study is focus on constructing procedure of BA system in KB card company, which is major credit card company in South Korea. In practice there were just few works that mentioned well-designed environment of KMS system, and other contribution of this study is to make a platform which invoke revelation of collective intelligence in data analytic professional users group.

• Knowledge Management Research
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• v.24 no.3
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• pp.127-148
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• 2023

Traditional Business Intelligence environment is limited to support the rapidly changing businesses and the exponential growth of data in both volume and complexity of data. Companies should shift their business intelligence environment into Self-Service Business Intelligence (SSBI) environment in order to make smarter and faster decisions. However, firms seem to face various challenges in implementing and leveraging the effective business intelligence system, and academics do not provide sufficient studies related including the success factors of SSBI. This study analyzes the three cases of Korean companies in depth, their development process and the assessment of business intelligence, based on the theoretical model on the key success factors of business intelligence systems. The comparative analysis of the three cases including project managers' interviews and performance evaluations provide rich implications for the successful adoption and the use of business intelligence systems of firms. The study is expected to provide useful references for firms to fully leverage the effects of business intelligence systems and upgrade towards self-service business intelligence systems.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.159-175
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• 2002

Web mining is an emerging science of applying modem data mining technologies to the problem of extracting valid, comprehensible, and actionable information from large databases of web in e-Business environment and of using it to make crucial e-Business decisions. In this paper, we present the noble framework of data visualization system based on web mining for analyzing the characteristics of on-line customers in e-Business. We also propose the framework of forecasting system for providing the forecasting information of sales/purchase through the use of web mining based on artificial intelligence techniques such as back-propagation network, memory-based reasoning, and self-organizing map.

• Journal of Korean Society for Quality Management
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• v.34 no.3
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• pp.31-40
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• 2006

Companies had to be more intelligent in order to survive in the rapidly changing environments. We need to make a decision to build the Information System to support the managers in their decision making. That is the reason many companies are tend to have Business Intelligence Systems. But, how can we know the new system would be better than the old system in making us intelligent? The answer is we can do it with the concept of Intelligence Density. In this study, Intelligence Density concept will be introduced, and the way how it can be applied to the information system will be presented. I think Intelligence Density should be studied more to help managers make right decisions for the DSS implementation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.84-90
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• 2019

Lately, there have been tremendous shifts in the business technology landscape. Advances in cloud technology and mobile applications have enabled businesses and IT users to interact in entirely new ways. One of the most rapidly growing technologies in this sphere is business intelligence, and associated concepts such as big data and data mining. BI is the collection of systems and products that have been implemented in various business practices, but not the information derived from the systems and products. On the other hand, big data has come to mean various things to different people. When comparing big data vs business intelligence, some people use the term big data when referring to the size of data, while others use the term in reference to specific approaches to analytics. As the volume of data grows, businesses will also ask more questions to better understand the data analytics process. As a result, the analysis team will have to keep up with the rising demands on the infrastructure that supports analytics applications brought by these additional requirements. It's also a good way to ascertain if we have built a valuable analysis system. Thus, Business Intelligence and Big Data technology can be adapted to the business' changing requirements, if they prove to be highly valuable to business environment.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.5
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• pp.489-500
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• 2002

As the interest in business interest is increased, data mining is increasingly used in BI as the core technique. To support Business Intelligence in e-business environment, the integrated data mining system which included in various mining operations should be able to flexibly integrate with database system and also it must provide the easy and efficient interface to implement the marketing process in various business applications. In this paper, we have implemented the EC-DaMiner system to support business intelligence in e-business area. The implemented system can be integrated with the conventional database system with the standard interface. Business applications can use MQL mining query language to discover the rules and mining result is modeled in marketing database, and the EC-DaMiner system make the implementation of business marketing process more easy.

• Journal of the Korean Society for information Management
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• v.28 no.1
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• pp.263-283
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• 2011

Business Intelligence(BI) is being used by the individuals who make decisions for management. Dashboard supports business intelligence by visualizing data, information, and knowledge so that they can be grasped at a glance. In this study, applications of dashboard were analyzed in the ARL libraries websites. Furthermore, the study suggested methods to establish and use the information system of the business intelligence and dashboard on the academic library websites in Korea. The findings of this study are expected to serve as the basic data to utilize the business intelligence and dashboard as a tool with which Korean academic libraries can demonstrate their value to the stakeholders in the academic community.

• IE interfaces
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• v.17 no.4
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• pp.426-439
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• 2004

This research discusses knowledge contents needed to build an OLAP system for medical sector, OLAP functionalities from past studies, and a medical intelligence system which is a kind of OLAP. The knowledge requirements which consist of nine contents and OLAP fundamental functionalities are applied to the system. Most past studies have focused on developing a medical data warehouse rather than OLAP. The medical intelligence system supplies health care providers (i.e., doctors, clinicians, researchers and nurses) and non-providers (i.e., managers and business analysts) with multidimensional OLAP functionalities. The system can be used to gain a deeper understanding of specific medical issues. In this research, we focus not on medical data warehouse, but on the technical challenges of designing and implementing an effective medical intelligence system for health care information. An architecture is applied to developing the medical intelligence system for a medical center in order to illustrate its practical usage. Six packages in the developed system are discussed in this research: Explorer, Analyzer, Reporter, Statistician, Visualizer, and Meta Administrator packages. Evaluation of the system and ongoing research directions conclude the research.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.2
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• pp.431-448
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• 2008

The change of management paradigm is that information technology change according to technology evolution at present is applied to corporate management, is that management level must be adapted to uncertainty management environment with activity and be made decision based on analyzed real time information through information system. This produces the effective target achievement and efficiency business productivity guarantee. At the present day, importation of business intelligence like enterprise information system has been the essential factor in business activities. Therefore, It is very important to give lessons the enterprises for building the business intelligence selecting the major success factors of more influence to managing results. In this paper, to authorize the research model and research constructions through theory study of literatures and surveying statics analysis prove the relational influences among the influencing factors related business intelligence system buliding.

• The Journal of Bigdata
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• v.8 no.1
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• pp.35-47
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• 2023

It is crucial to develop effective and efficient big data analytics methods for problem-solving in the field of business in order to improve the performance of data analytics and reduce costs and risks in the analysis of customer data. In this study, a big data-driven data analysis system using artificial intelligence techniques is designed to increase the accuracy of big data analytics along with the rapid growth of the field of data science. We present a key direction for big data analysis systems through missing value imputation, outlier detection, feature extraction, utilization of explainable artificial intelligence techniques, and exploratory data analysis. Our objective is not only to develop big data analysis techniques with complex structures of business data but also to bridge the gap between the theoretical ideas in artificial intelligence methods and the analysis of real-world data in the field of business.