Data mining is very powerful technology which is developed to help companies in analyzing the data and collect useful information related to customer behavior and preferences. Data mining is used in businesses in order to discover useful data from data warehouses so that business decisions are taken effectively. The importance of data mining in businesses is to turn the raw data into useful data and also helps companies in increasing their sales and develops effective marketing campaign and so on (Han, et al., 2011). The use of data mining will enhance the competitiveness as well as help in identifying the customer segments and customer behavior.
Moreover, businesses could use the data mining to a large extent because it provides some valuable business information from a large database. The businesses use data mining for improving the business efficiency to a large extent because data mining help in analyzing a large data and help in identifying hidden information (Aggarwal & Zhai, 2012). The benefits of using data mining in businesses is that data mining will help in achieving the competitive advantage, high value, etc. In addition to this, the result of data mining for businesses will be more profitable as using data mining is easy similarly like e-mails.
This article discusses use of data mining in business analytics to support the business competitiveness. In this paper, different use of data mining and business intelligence is also defined. Moreover, it will highlight on using the data mining for enhancing the business competitiveness.
In today’s competitive environment, businesses are required to maintain the large amount of data and for which they are using effective technology like data mining. Data mining is also used by businesses in order to collect and maintain the large amount of data and analyze the data for identifying the hidden information efficiently and quickly (Lee, 2013). Moreover, the use of data mining help in business analytics and this lead to identify the valuable information which help the companies to compete effectively in highly competitive market place. The use of data mining is like a strategy where data mining process takes place in four phases like data presentation, data mining technique, generating data mining result, using cluster model with data mining. However, these all four phases is used efficiently by businesses in order to achieve competitive advantage. Further, e-businesses also use data mining process to identify the real-time customer segments, and credit risk which creates critical impact on the business competitiveness and reduces the business risk (Kantardzic, 2011) . This e-businesses data mining is related with the business analytics and business intelligence where ability to generate the business intelligence is provided to businesses so that they can compete easily with their competitors.
From this article study, it can be concluded that use of data mining increases the data resources which help in growing a business to high level. In addition, it also identified that the ability to use data mining from massive amount of data resources improves the different areas of business i.e., operation activities, customer, supply chain and many more.
Aggarwal, C. C., & Zhai, C. (2012) Mining text data. USA: Springer Science & Business Media.
Han, J., Pei, J., & Kamber, M. (2011) Data mining: concepts and techniques. Elsevier.
Kantardzic, M. (2011) Data mining: concepts, models, methods, and algorithms. USA: John Wiley & Sons.
Lee, P.M. (2013) Use of Data Mining In Business Analytics to Support Business Competitiveness. Review of Business Information Systems. 17 (2). pp. 1-6.
This report discusses data mining issues related to security and privacy issues which individual or firms faces. While Data mining is an effective process for analyzing the data from different perspectives in order to achieve useful information. In simple words, data mining can be defined as Knowledge Discovery Database (KDD) which helps in discovering the data which is implicit or uncovered or useful information from the databases. In similar manner, privacy issues in data mining will also be determined (Hashem, et al., 2015). Further, this report will also highlight some ethical implications which are need to be considered while data mining and in context to that, importance of those implications in data mining process will also be discussed. Thus, this study will help in understanding more about data mining and different issues which are faced while data mining process.
The aim of this report is to identify some major security issues in data mining process. For achieving this aim, the following objectives are to be accomplished:
- Identify security and privacy issues in data mining
- Determine ethical implications and its importance in data mining
The major data security issues observed in data mining process is data integrity. Data integrity is big challenge because analyzing data is easy but integrating a data from different sources is challenging task. However, data integrity increases the security issues while data mining because when data of any individual is transferred from one system to another then at that time, security for information may get lost (Witten, et al., 2016). Due to data mining, it becomes possible to analyze the business transaction and identify significant information about the customers (individual) habits and preferences.
In respect of this, Kaisler, et al., (2013) stated that security issues also involve issue related to cost of data mining. From last few years, the cost of system hardware is declining simultaneously whereas data mining and data warehousing tend to be self-reinforcing. In concern to this, it is identified that the greater the utility of data, the great is pressure to maintain and collect the large amount of data (Xu, et al., 2014). So, the major issues in data mining is mining methodology and user interaction because data mining query language, incomplete data and many more are faced by the individual and companies.
The privacy issues in data mining process are high because there are chances of leaking of individual private information to a large extent. The privacy issue is raised when the users are allowed to analyze the private or personal information constantly. For dealing with privacy issues in data mining, privacy preserving data mining (PPDM) is developed for safeguarding the sensitive and private information from any unsolicited disclosure (Hashem, et al., 2015). However, the privacy issues found in data mining is that sometimes data which is analyzed provided false data and that created impact on the stability and performance of the companies. However, the security issue in data mining is raised due to technology and social factor and that develops problem related to individual privacy. The individual privacy is major reason for using more data mining process as it help in providing privacy and security protection to their information.
In different sectors, data mining has been successful used for analyzing the different databases like in retail sector, marketing, e-commerce sector and many more. The use of data mining provides support to firms in analyzing the consumer behavior and also to predict the future trends and this helps in enhancing the firm’s revenue, reduce cost and increase profit. In context to it, ethical implication for businesses by using data mining will be different from legal implications because when the consumer data is used by the companies for targeting them back will develop unethical issues (Sin & Muthu, 2015). But with the help of data mining process, company can get only useful information about the customers and this will create ethical implication.
Moreover, the ethical implication of data mining in healthcare sector is also effective because it is necessary to keep the patient information and for that patient consent is required for gathering the information. This collection of data can be used by other research companies ethically i.e., after all security checks (Papamitsiou & Economides, 2014). However, these ethical implications in data mining will help different sectors in maintaining the transparency while mining the collected data as this will create the security to data and also avoid the misuse of customer data.
The importance of above stated ethical future implication will creates positive impact on data mining process to a large extent. The importance of ethical implication is that it will provide the data accuracy as data mining process involve large amount information from many diverse sources (Mittelstadt & Floridi, 2016). The extraction of hidden data or predictive information from large databases can be effective identified with the help of data mining. As data mining is an effective powerful technology which help companies to identify and focus on potential information from their data warehouses.
For instance, a bank may maintain also its credit cardholder’s information in different databases and for that each cardholder name may be different from each other and in this ethical data mining will help in translating the data from one system to another and also help in selecting the address recently entered. At the same time, the development of data warehouses has increased the importance of ethical implication through database security (Hair, 2015). So, in ethical sense, database security is highly related with the privacy of data and this also enhances the individual capacity to access to their data easily.
From above study, it can be concluded easily that data mining process is very effective way to extract the data which is uncover. With the help of this study, it is also identified that security issues and privacy issues are developed due to ineffective use of mining methodology as well as the cost of system hardware. Moreover, the ethical implications in data mining will also create transparency in the process and also help in providing more security and privacy to the useful information. At last, it can also be summarized that data mining has become one of important feature for managing the security and privacy while analyzing and mining the data.
Hair, J. F. (2015) Essentials of business research methods. UK: ME Sharpe.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015) The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, pp. 98-115.
Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013) Big data: Issues and challenges moving forward. In System Sciences (HICSS), 2013 46th Hawaii International Conference on (pp. 995-1004). IEEE.
Mittelstadt, B. D., & Floridi, L. (2016) The ethics of big data: Current and foreseeable issues in biomedical contexts. In The Ethics of Biomedical Big Data (pp. 445-480). USA: Springer International Publishing.
Papamitsiou, Z., & Economides, A. A. (2014) Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Journal of Educational Technology & Society, 17(4), pp. 49.
Sin, K., & Muthu, L. (2015) APPLICATION OF BIG DATA IN EDUCATION DATA MINING AND LEARNING ANALYTICS–A LITERATURE REVIEW. ICTACT journal on soft computing, 5(4).
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016) Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Xu, L., Jiang, C., Wang, J., Yuan, J., & Ren, Y. (2014) Information security in big data: privacy and data mining. IEEE Access, 2, pp. 1149-1176.
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