DATA 43 terabytes of data which is bigger

by Wahida Abd Wahab
Faculty of Information Management, Universiti Teknologi MARA (UiTM)
Shah Alam Selangor
[email protected]
In today, the information technology has changed the way of marketing system that cover how organization organize and arrange about customer information (Shaw et al., 2001). However, the accessibility of big size of customer’s data, new information technology tools have produced opportunities and challenges for the business in order to increase competitive advantage (Rashidi, Nosratabadi and Jalalabadi, 2015). According to M. Graen Shawl et al as cited by Shaw et al. (2001) provide example that Wal-Mart, the biggest vendor in the US for a customer databases, contain about 43 terabytes of data which is bigger than the databases used by the internal Revenue Services for collecting income taxes.
According to Rashidi et al. (2015) mentioned that many organization realized that the knowledge contain in this wide database is an important factor to support several organizational decisions making. The customer knowledge of this database is suitable for marketing and performing. In addition, the marketing decisions are very significant to organization in order to increase their profit as well as can affect customer behavior (Kumar et al., 2011). Refer to the previous studies, explained that the marketing is becoming more difficult every day due to the business environment are more complex and marketing decisions are limited by the surrounding and society (Ali and Bach, 2014). To support this review, previous study suggested the marketing decision and should be consistent which will help marketers to do business in professional ways (Aylmer, 1970).
Thus, to ensure that the marketing decision become more accurate, some technique and tools should be introduced and use in the organizations. Refer to Ali and Bach (2014) data mining and knowledge management are the best technique and tool that can be used to support the marketing decisions. Other researchers also agreed that the both technique and tools will support or assist marketing decisions as well as increase organization performance. According to Shaw et al. (2001) support that data mining is important tool to support marketers and improve marketing significantly as well as will assist organization to analyze the overall market.

Apart from that, Ali and Bach (2014) also mentioned that good marketing decision is based on knowledge about customers. The types of knowledge is very important and has been considered as a main for marketing functions. Shaw et al. (2001) explained that the critical knowledge can be identify and found in the organization’s databases but most of it is hidden. Therefore, data mining tools has been introduce to decision maker in order to assist them to get the useful hidden knowledge to ensure better decision will be make (Ali and Bach, 2014). In addition, knowledge management also one of the main factor that affect the marketing decision in the organization. Alavi and Leidner (2001) explained that knowledge has been recognized as a significant organizational resource that will assist the marketers to obtain the knowledge easily. Data Mining and knowledge management is related because data mining is essential part of knowledge management initiative.
This paper will discuss the data mining and knowledge management as a tools or techniques to support marketing decision. The reason of this study is to identify and discuss the theory that support data mining and knowledge management as tool and techniques for marketing decision. It is because according to previous studies, mentioned a few issues and challenges in knowledge management and data mining which is still an evolving field and need to be addressed. “Some of them is related to the data mining techniques and some knowledge management about detection process, while others are related to knowledge management” (Rashidi et al., 2015).

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Literature Review
In this study the literature review will use and focus on theoretical review. This study will identify and discuss the relevant information that will support the theory of data mining and knowledge management for marketing decision.

Data Mining
According to Shawl as cited by Ali and Bach (2014) data mining play an important role in different areas to help to predict and support the decision maker in organization. Data mining can be defined as an essential step in the knowledge discovery in database (KDD) process that produces useful patterns or models from data (Silwattananusarn and Tuamsuk, 2012). Data mining also is a search process and analyzing data to find the right information that includes exploring, selecting, and modeling of data to expose previously unknown pattern and eventually gives reasonable information from the databases (Rashidi et al., 2015).

Knowledge Management
Marketing Decision
Theoretical Framework
The figure 1 shows the theoretical framework of independent variable and dependent variable for this study. The theoretical framework was adopted from the previous studies by Ali and Bach (2014) to identify data mining and knowledge management as a techniques and tool for marketing decision. The illustration of theoretical frameworks as below:
678927145564Data Mining
00Data Mining

2570667638743700145224603Marketing Decision
00Marketing Decision

67903999284Knowledge Management
00Knowledge Management

Figure 1: Theoretical Framework
Alavi, M. & Leidner, D.E. (2001). Knowledge management and knowledge management systems: conceptual foundation and research issues. MIS Quarterly, 25(1), 107-136
Ali, A.E. & Bach, C. (2014). Data mining and knowledge management for marketing. International Journal of Innovation and Scientific Research, 2(2), 321-328
Aylmer, R.J. (1970). Who makes marketing decision in the multinational firm? Journal of Marketing, 34(4), 25-30
Rashidi, R.P., Nosratabadi, D.A. & Jalalabadi, H.K. (2015). Knowledge management and data mining for marketing. Journal of Applied Environment and Biological Sciences, 5(5), 277-285
Shaw, M.J., Subramaniam, C. Gee, W.T. & Welge, M.E. (2001). Knowledge management and data mining for marketing. Decision Support System, 21, 127-137


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