Reasons, Benefits, And Impacts Of Data Mining In Businesses

Reasons behind using data mining in businesses

1.Why Datamining is used in Businesses? Discuss.

2.Identify the Major Security, Privacy and ethical Implications in Data Mining.

Importance of data mining

Data mining is used to find out different patterns or a particular type of information from big data system. It helps to find out different kind of knowledge and contents from huge amount of data. The data mining process is helps in computer decision making system and information processing. It is used in finding different patterns like cluster, anomaly. Data mining is important for its impact in business and industry.

How business could use data mining

Business can use data mining in different way. It can help a business organization to understand the customer behavior towards the business offerings. It finds out those unknown credible patterns which are important to do the business successfully. Data mining is also used in the analysis of various data and in decision making of the organization. Data mining helps in improving the customer loyalty, discover hidden profitability and to decrease client churn.

Results and benefits of using data mining

There are many benefits of using data mining other than business fields. Data mining can also be used in health, engineering, research and statistical analysis. In medical field data mining can find out different patterns of the deceases. In engineering data mining solves various problems regarding a system and especially it helps to create an automatic system with the help of data analysis decision making techniques. In research data mining can be a key part of finding various patterns and knowledge related to the discovery.

Introduction

Data mining is a technique that is used to find the patterns and extract information from a big amount of data system. Data mining is combination of various features like machine learning, statistics and database systems. It is an important method which can be used in data analysis and decision making in business.

In this article different aspect and impacts of the data mining method are discussed in details.

Data mining is the technique to search automatically the large amount of data to find patterns and trends that cannot be detected by normal analysis (Wu et al., 2014). It uses different advanced mathematical and statistical algorithms to analyze the data and to evaluate the chances of happening different events.  It simply extracts the knowledge from huge amount of data (Larose, 2014). The main properties of data mining are automatic detection of patterns, prediction of likely outcomes, creation of actionable information and to focus on huge data sets and databases.

How business could use data mining

Business can use data mining in different way. It can help a business organization to understand the customer behaviour towards the business offerings. It finds out those unknown credible patterns which are important to do the business successfully. Data mining is also used in the analysis of various data and in decision making of the organization (Witten et al., 2016). Data mining helps in improving the customer loyalty, discover hidden profitability and to decrease client churn. Data mining is also applicable for the big data analysis of organisations having a huge amount of data base. This technique helps to quickly find out the key factors of the marketing strategy of the organisation.

Conclusion

From the above discussion it is concluded that the data mining technique is an important tool in data analysis and decision making. In case of business data mining is especially useful to understand the customers choices and to analysis the data regarding the profit and income of the company

The aim of this report is to discuss about the major security issues, privacy issues, and ethical implications in data mining. Data mining extracts various patterns and a particular type of information in a big data set on the basis of mathematical and statistical analysis. Sometimes data mining is really helpful in business for data analysis and decision making. From the security and privacy point of view data mining can sometimes predict personal information about any individual on the basis of their data supply. This can be a real issue as the security of the personal information is hampered.

In this report the security and privacy issues related to data mining is discussed with the ethical implications of it.

Major security issues in data mining

Public security is important and is given highest priority by any organisations. People’s privacy should be cared of in case of applying any new technology such as data mining. Data mining finds out several patterns and similar type of information from inter related big set of data on the basis of some mathematical algorithms. Data mining is used by several business organisations and internet based retailers who collect a huge amount of data from the customers of their product (Braha, 2013). During several transactions customers are often asked to provide information regarding them from these organisations. Data mining can analyze these data and find out the information regarding the choices and the background regarding the buying habit of the customers. In case of different transaction various organisations can get the idea about the people’s habit of using products and that may also influence the industry to make qualitative changes in the products to draw more profit. Data mining can be an important weapon for the hacker who may use it against the target system or individual. Hackers can easily get the information from a computer or network system of an organisation or individual. With the help of data mining they can extract the confidential information about the organisation or individual and make serious damage to their security. Terrorists can also get the knowledge about the security patterns of a country in different places with the help of data mining. They can find out the loop wholes in those security systems from the data analysis. Thus data mining can be a security for the national defence and organisational security also. There are many more security issues that can be generated in future on the basis of data mining as this process takes the help from probabilistic approach (Cuzzocrea, 2014). Many security issues cannot be even identified previously due to the approach of this application. Thus there is always a chance of uncertainty and risks of making the data insecure of the data base of a system.

Results and benefits of using data mining

Privacy issues in data mining

With the introduction of data mining and additional data sharing the attention has increased on the implications of privacy. Concerns have increased on the basis of the privacy of different projects and data mining applications as they are increasing various numbers of applications beyond their original purposes. The main thing of this data mining process is that the way of application should be determined and the purpose should be restricted only for betterment of the service and research of complex problem. Whenever data mining is used to understand the customer preferences and other personal issues, then it can be a serious privacy problem. Organisations and government extract data from the individual transactions and get the idea about the choices and background activity of public (Demšar et al., 2013). As data mining uses probabilistic approach, it can also predict about the individual activities. This type of analysis can lead to serious privacy issues. Governments and organisations can take the advantage of these type of analysis for their own interests and that may lead to affect the privacy of individuals. As a result this can lead to even the loss of public reliability on the data system. People are now more conscious about their security regarding the data base system of different organisations (Lu et al., 2014). There is no particular law restricting the use of data mining in public security. Only technical solution of such problem is not enough to stop the privacy issues regarding data mining.

Ethical implications in data mining

Ethical implication of data mining is an important topic to be discussed from the security and privacy point of view. With the development of technology and distribution of Internet the use of online transaction of public information has increased to a bigger extent. It is seen that that in recent time public reliability has also increased to a bigger extent as the amount of privacy issues have scaled up (Xu et al., 2014). Users are more concerned about the theft of stealing their personal information in internet. They know that it is easy to collect, extract and access their data for the organisations and the government. As a result people are asking for the security of their data and demand the control of such information on them. Thus it can be said that the activism among people is increasing regarding the security and the privacy of their information (Zhu et al., 2014). Organisations and government should also think about such activism of public and they should find out the way of using several techniques like data mining. They should make their policies in the application of data mining keeping the security and privacy related issues in their mind.

Conclusion

From the above discussion it is concluded that data mining should be used in an ethical way so that the privacy and security related to the public information is not distorted. Data mining extracts various patterns and a particular type of information in a big data set on the basis of mathematical and statistical analysis. Sometimes data mining is really helpful in business for data analysis and decision making. There is no doubt that data mining is a useful technique to analyze data and to make a decision on the basis of the computing power. It should be followed that the security and privacy of public must be given highest priority in case of using such methods.

References

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

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

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.

Braha, D. (Ed.). (2013). Data mining for design and manufacturing: methods and applications (Vol. 3). Springer Science & Business Media.

Cuzzocrea, A. (2014, November). Privacy and security of big data: current challenges and future research perspectives. In Proceedings of the First International Workshop on Privacy and Secuirty of Big Data (pp. 45-47). ACM.

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

Lu, R., Zhu, H., Liu, X., Liu, J. K., & Shao, J. (2014). Toward efficient and privacy-preserving computing in big data era. IEEE Network, 28(4), 46-50.

Xu, L., Jiang, C., Wang, J., Yuan, J., & Ren, Y. (2014). Information security in big data: privacy and data mining. IEEE Access, 2, 1149-1176.

Zhu, H., Xiong, H., Ge, Y., & Chen, E. (2014, August). Mobile app recommendations with security and privacy awareness. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 951-960). ACM.