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Explains the role of data analysis tools and data mining in contemporary organisations.
Identifies and explains the ethical implications around gathering, storing and using customer information.

Data mining for gaining insight of consumer needs

The organisations functioning in the current era always search for effective methods for improving their operational procedures to enhance their competitive supremacy in the operating markets. In order to increase the revenue margins, the global industries attempt to raise their consumer base by obtaining necessary information about the consumers for reaching as well as attracting them to consume their products (Cambria et al., 2013). Moreover, it is of utmost importance for an organisation to record the information about their potential customers for reaching them when needed. Hence, it is observed that evaluation mechanisms and utilisation of data software has developed in the market for segregation of market information and gaining an overview of the current trends in order to modify their business strategies.

Data mining is a group of data analytic mechanisms, which is utilised in the past to monitor and identify the fraudulent operations. This tool is highly valuable to control the security of an organisation, especially in the healthcare sector. Therefore, the modern organisations have adopted this technique due to its widespread advantages. However, it is of utmost importance for those organisations to understand the roles and functions of data mining effectively to ensure ethical exploitation to collect and store the information of the customers (Demšar et al., 2013).

It has been observed that the organisations functioning in a complex business environment develop business reports through their available knowledge and expert skills. As a result, it enables the higher-level management to initiate actions, which could boost up the overall organisational performance. The data analysis tools are crucial to gain an insight of the main performance indicators, which are valuable to collect and store the customer information in an effective fashion (Ivezi? et al., 2014). 

The data mining process, on the other hand, is not associated with information evaluation; instead, it starts with accumulating, integrating, preserving and filtering the data valuable for examining the current frameworks. This is mainly intended to assure that such frameworks deliver the most desirable outcome (Dean, Payne & Landry, 2016). In the existing scenario, the human beings often use different online sources, in which they are needed to provide detailed personal information. However, these individuals are not unaware of the fact that such information is stored on the part of the organisations for future use. In this case, it is noteworthy that the customers might not desire to have their private details spread to other sources from the organisations. This mandates the need for the organisations to adopt the process of data mining for classifying the customer information into general and personal information.

Data analysis tools for collecting and storing customer information effectively

Such segregation of data enables the organisations to ensure the security of private information in an appropriate database by restricting them to distribute to other sources (Larose, 2014). The data mining process is conducted by seeking help from the analytical engineers through their existing skills and expertise for establishing the ultimate outcome. In the past, the process of data mining has been valuable only to the end users. However, with the passage of time, the global industries have been engaged in using this tool for ensuring proper functioning of the ad hoc issues and enquiries to develop an extensive report. The mechanism related to data mining takes into account the categorisation of information from a large data pool and thus, it recognises the information patterns and trends (Rokach & Maimon, 2014).

The data mining process is channelled through the general structure and thus, the probability of malpractices is minimised. The data mining process along with its rile of segregation enhances the knowledge base of the organisations, which improves their activities in the form of greater service quality. This increases the satisfaction level of the customers, as they perceive that their privacy is ensured effectively (Lausch, Schmidt & Tischendorf, 2015). The ability of the organisations in segregating data requires keenness for improving an efficient quality in relation to the variations of changes in velocity, life span and dependence of the pertinent data set.

The role of data mining and other related equipment is to obtain appropriate data for enhanced business performance, since it gains knowledge about the pertinent activities of the organisations by obtaining rightful information. The outcomes developed through these tools are valuable to discover the final outcomes, which the management investigates to obtain knowledge whether such tools are appropriate and helpful in undertaking effective business decisions (McCue, 2014). A correct technique of data mining is critical to the business growth along with raising customer satisfaction level towards the organisations. The mechanisms pertaining to data analysis and mining increases the value to related to information and technological department of the organisation. This department publishes the reports that are often perceived as useful for effective procedure of data mining and thus, it raises the significance of this specific department (Roiger, 2017). In addition, the procedure of data mining assists the organisations in gaining an overview of the existing trends in the market and customer needs, which helps in making crucial business decisions.

There is presence of a diverse range of ethical challenges, which is associated with accumulating, securing and preserving the customer information in the organisational database. The organisations gather and record a group of data associated with the customers in their databases, which are mainframe centrally. The ethical issues associated with the data are examined by taking into account three factors. These three factors include the ethical obligations of an organisation towards its customers, ethical responsibilities of the staffs towards the organisation and the ethical responsibilities of the customers towards the organisation (Shmueli, Patel & Bruce, 2016). The accumulation and storage of information is critical to improve the customer service for ensuring the overall business growth of a firm.

Ethical challenges associated with accumulating, securing, and preserving customer information

Thus, it is necessary to ensure ethical collection of the information from the customers and the customers are not forced to provide any private information beyond their desires (Tan, P. Steinbach & Kumar, 2013). The staffs of the organisation has a number of ethical responsibilities, which is carried out by restricted browsing of the customer data for avoiding sale of the same in the outside market to the associated parties. The customers, on the other hand, are liable to share specific information to the firm, which helps in minimising the degree of falsified information. As a result, it helps in improving the outcomes discovered through evaluation of such information (Witten et al., 2016). 

It has been observed that ethical reinstatement, accumulation of customer-related data and enhanced awareness of the staffs in curbing the distribution of private information increases the motivation level of the customers. As a result, the customers share their actual information, since they are ensured about the maintenance of their privacy. Hence, it is evaluated that ethics swathe the mechanism related to recording and accumulation of information through adherence to the privacy laws (Wu et al., 2014). The procedure of information accumulation from the consumers attempts to disclose the kinds of products, which the consumers are buying and with such information, it attempts to find out the factors and time span related to such purchases.  

The other implications of ethics shed light on the accuracy of data, as inaccurate data would lead to falsified outcomes, which could degrade the life of the customers in an indirect manner. The organisations, thus, attempt to accumulate the information in the form of convincing the customers. This is achieved through the mechanism of data storage for obtaining specific and rightful data, which would assist to publish the feasible results leading to pertinent business decision-making and overview of the needs and wants of the customers (Zaki, Meira Jr & Meira, 2014). Hence, if the organisations succeed in maintaining the ethical implications, it would assure the customers regarding their information safety and security. It is found out that the organisations are required to accumulate information through various steps for gaining an insight of the consumer needs. Accordingly, they could provide essential and widespread services to the customers for increasing their competitive edge in the operating markets.

Conclusion:

The existing paper aims to develop an insight of the data mining process and mechanisms of data analytics, which the business organisations have initiated for enhancing their overall business operations. In addition, detailed discussion has been made about the role of data analysis and data mining and their importance in classifying general and private information of the customers along with ensuring security of the private information. The latter segment of the paper has provided a detailed overview of the ethical implications in relation to accumulation and recording of consumer information rightfully for safeguarding such information. This is mainly intended to attain the confidence of the customers for obtaining their actual information to arrive at the correct ultimate outcome.

References:

Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems, 28(2), 15-21.

Dean, M. D., Payne, D. M., & Landry, B. J. (2016). Data mining: an ethical baseline for online privacy policies. Journal of Enterprise Information Management, 29(4), 482-504.

Demšar, J., Curk, T., Erjavec, A., Gorup, ?., Ho?evar, T., Milutinovi?, M., ... & Štajdohar, M. (2013). Orange: data mining toolbox in Python. Journal of Machine Learning Research, 14(1), 2349-2353.

Ivezi?, Ž., Connolly, A. J., VanderPlas, J. T., & Gray, A. (2014). Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data. Princeton University Press.

Larose, D. T. (2014). Discovering knowledge in data: an introduction to data mining. John Wiley & Sons.

Lausch, A., Schmidt, A., & Tischendorf, L. (2015). Data mining and linked open data–New perspectives for data analysis in environmental research. Ecological Modelling, 295, 5-17.

McCue, C. (2014). Data mining and predictive analysis: Intelligence gathering and crime analysis. Butterworth-Heinemann.

Roiger, R. J. (2017). Data mining: A tutorial-based primer. CRC Press.

Rokach, L., & Maimon, O. (2014). Data mining with decision trees: theory and applications. World scientific.

Shmueli, G., Patel, N. R., & Bruce, P. C. (2016). Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner. John Wiley & Sons.

Tan, P. N., Steinbach, M., & Kumar, V. (2013). Data mining cluster analysis: basic concepts and algorithms. Introduction to data mining.

Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.

Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. ieee transactions on knowledge and data engineering, 26(1), 97-107.

Zaki, M. J., Meira Jr, W., & Meira, W. (2014). Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press.

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