A knowledge management approach to data mining process for business intelligence

Abstract

Purpose-Data mining (DM) has been considered to be a tool of business intelligence (BI) for knowledge discovery. Recent discussions in this field state that DM does not contribute to business in a large-scale. The purpose of this paper is to discuss the importance of business insiders in the process of knowledge development to make DM more relevant to business. Design/methodology/approach-This paper proposes a blog-based model of knowledge sharing system to support the DM process for effective BI. Findings-Through an illustrative case study, the paper has demonstrated the usefulness of the model of knowledge sharing system for DM in the dynamic transformation of explicit and tacit knowledge for BI. DM can be an effective BI tool only when business insiders are involved and organizational knowledge sharing is implemented. Practical implications-The structure of blog-based knowledge sharing systems for DM process can be practically applied to enterprises for BI. Originality/value-The paper suggests that any significant DM process in the BI context must involve data miner centered DM cycle and business insider centered knowledge development cycle.

Linking business intelligence to a knowledge-based sustainable competitive advantage in organizations

“…The question has always been, "how can we use the information at our disposal better to assist in making better informed decisions?" BI primarily helps provide historical, current and predictive views of business operations from data stored in the data warehouse, which provides organizations with an opportunity to make effective business decisions (Wang and Wang, 2008). These technologies, processes, and applications analyze structured and unstructured organization data and business processes within the organisation.…”

“…Demand for BI applications continues to grow even at a time when demand for most IT products is soft (Parenteau et al, 2016). According to Wang and Wang (2008), organisations utilising massive data to gain competitive advantage seems to be the central theme of business intelligence deployment. There is a perception that business intelligence and knowledge management are two independent information systems domains (Wang and Wang, 2008).…”

“…According to Wang and Wang (2008), organisations utilising massive data to gain competitive advantage seems to be the central theme of business intelligence deployment. There is a perception that business intelligence and knowledge management are two independent information systems domains (Wang and Wang, 2008). Elbashir et al (2008) make the case that with business intelligence, organisations can improve decision making and through better informed decisions, gain competitive advantage.…”

Knowledge Management Strategy to Improve Business Sector

“…Stackowiak et al (2007) define BI as the process of taking large amounts of data, analyzing those data, and presenting a high-level set of reports that condense the spirit of those data into the basis of business activities, which enable management to make fundamental daily business decisions. BI refers to various software solutions (technologies and methodologies) to acquire the right information which is necessary for the business decision-making with the major purpose of enhancing the overall business performance on a marketplace [Wang & Wang, 2008]. It is a new working culture with information and a specific methodology to work with information and knowledge, open communication, and knowledge sharing [Negash & Gray, 2008].…”

Business intelligence and the role of external information in small- and medium-sized enterprises of the Finnish marine industry

“…Commonly used BIS include data, text and web mining, data warehousing and visualizationbased tools (Chau et al, 2007;Chung et al, 2005;Popovič et al, 2012;Wang and Wang, 2008;). External information for BI is usually acquired from competitors, customers and the business environment via interviews, news reports, patent data and the Internet (Li et al, 2007;Shih et al, 2010;Tekic et al, 2012).…”