1.The data mining includes the retail, finance, healthcare and the manufacturing which is considered important for using the tools and techniques. The business is based on discovering the patterns in order to make the business decisions for the sales trends, development of the smarter marketing campaigns and then predicting all the customer loyalty functions. The specific uses are based on:
- The market segmentation which is to identify about the customer characteristics and then work on the products of the company
- The fraud detection and customer churning will help in identifying the transactions.
- The direct marketing and the interactive standards are for the individual accessing of the website which is considered to be obtained at a higher response rate.
The automated prediction of the trends and the behaviours helps in handling the processes where there is a targeted marketing standard for maximising the returns on the investments. There are predictive problems for the targeted marketing, which includes the use of the data on the promotional mails to identify about the targets with maximising the return on the investments. (Marinakos et al., 2016). The automation process with the unknown patterns are set to determine about the hidden patterns with the use of business rules that are for the competitive advantage. The increased revenues are from the credit card operations which are tested through the non-intuitive possibility. The business trends are based on the knowledge driven decisions with the check on how the retail companies adapt the data mining with the segments set for the regency, frequency and monetary groups. The customer works with the utilities which includes the terms that are used for the customer and for the collation of the billing information, handling the customer services interactions, visits to the website and the other metrics.
Article/news item relating to data mining in business
As per the analysis, the articles about how the business is able to work on the data mining with the specific sample pool. The data footprints help the users to collect and share the information directly with the companies with the evolvement of the complete control of the personalised data. The users can easily reclaim the control of the data with the empowering and setting of the price or the barrier to access. The technology is based on the fact how there is a possibility to ensure the privacy with the storage of the data locally on the device of the user, with the normalised and aggregated form of the data. The specific business operations are to handle the personal data backup. The data mining helps in the empowering of the consumers to set the digital footprint. The company Digi.me has been focusing on making the future for the individuals where there is a build-up of the scalable business for mobile, where the personalised data platforms are based on individual sharing values set in between both sides of transaction. The personalised data company is evolving with the individuals to become aware of the online privacy and setting the personal dataset online.
2.Security: The security is based on the processing with creating the sequence with the queries to extract information with the large amount of the data. The data mining techniques can easily be used for the recovering of the problems with the database security. The growth of development is based on the primary challenges with the consumers that will encounter the data analysis without any giving of the right to use the information for any specific forms of the records. The development of models can easily lead to the reduced security where the users might face certain issues as well. The data mining is based on extracting the information where the companies include certain forms of the security issues. (Huang et al., 2016). There are companies who need to monitor the access for the data and check with the parts of warehouse to handle the access.
Privacy: The data mining standards set with the privacy and the legal issues are considered to be the growing conflicts where there are governmental and the corporate entities that would lead to setup of the information amount. The parts include the concern where the data is collected with stored data warehouse, where the access is based on information. The technologies are based on the extraction of data comes with finding different information and relationships for the customers and then extracting the data. This leads to the customer’s information collection about him/her. The technologies are available where the data mining could be for the extraction of the data from the data warehouse. This helps in finding the different information and the relationship for the customers with making connections that are based on the extraction. This would be able to put the customer information as well the privacy at risks. (Pereira et al., 2016). The data mining is mainly for the arrangements of the data and then to cover the consumer information which includes the confidentiality and the privacy. The way is through the data aggregation where the data could easily be handled in the form of different sources.
Ethical Concerns: The major use of the data mining includes the serious implications where the companies generally seem to face some ethical dilemma where there is a need to decide if the company should be aware of the personal information or not. (Ryoo, 2016). For this, there is a need to check on how hurt the competitive advantage is in the market place, with the check on deciding about the lack of the ethical concerns which leads to the loss of the consumers. The company make use of the data mining and then work on the awareness programs that are for the different applications. The consideration is about the wisdom.
3.Considering the company like Walmart, there have been approach set for the restricted extensive database where the storage is of the stocks, stores and the data that is collected. The companies have the products which are allowed under the database of Walmart, where there are companies to handle the mining with the information that related to the sales of the product. The restriction of the accessibility with the companies to work on the product offers is based on the accessibility where Walmart has been able to show the concern of the security and the privacy when it is for the data mining. (Shmueli & Lichtendahl, 2017).
Considering the privacy of IBM which works on the different methods of the mining. Here, there is a need to work on the individual factors where there is a creation of accurate models. The IBM works on the development of privacy preservation, where there is a randomisation of the information with the transfer of the data. The data mining includes the gathering of information and not impeding to the rights of privacy of the customer. There are different companies which works on the governmental analysis with the use of the data mining for the jobs. Hence, for this, there is a need to check on the quick transfer and processing that will make it easy for the employees to identify the theft risks. (Shmueli et al., 2016). The privacy concerns are important for the data mining with the risks evaluated through it. There are concerns about how the consumers could buy the product and not become conscious of the technology of data mining.
The ethical concerns for the company includes the use of the data and then work on the discrimination of the people based on the racial and the sexual orientations. The data mining is considered to be illegal where the individuals need to be protected from any type of the unethical activity. This will include the decision-making process and know about how the information could be used. Through this, there are certain straightforward consequences which are for making use of the information and relating to the privacy and individuality. The wrong use of the data could easily be caused when the people fail to handle the unethical issues. (Tasioulas, 2016). It is considered to be illegal as there is a major focus on the value and the protection so that there is a possibility to work with the threats and the dangers to discuss about the different issues. The experts consider the data mining to be neutral with the data that is for the questions and concerns related to ethics.
Huang, D.W., Chen, J.L., Deng, P. and Lü, L., 2016, December. Big Data Mining and Intercultural Business Discourse Studies: A Case Study of Li Ning's Corporate Social Responsibility Reports. In Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII), 2016 International Conference on (pp. 119-122). IEEE.
Marinakos, G. and Daskalaki, S., 2016. Viability prediction for retail business units using data mining techniques: a practical application in the Greek pharmaceutical sector. International Journal of Computational Economics and Econometrics, 6(1), pp.1-12.
Pereira, S., Torres, L., Portela, F., Santos, M.F., Machado, J. and Abelha, A., 2016. Predicting Triage Waiting Time in Maternity Emergency Care by Means of Data Mining. In New Advances in Information Systems and Technologies(pp. 579-588). Springer, Cham.
Roiger, R.J., 2017. Data mining: a tutorial-based primer. CRC Press.
Ryoo, J. ‘Big data security problems threaten consumers’ privacy’ (March 23, 2016) theconversation.com https://theconversation.com/big-data-security-problems-threaten-consumers-privacy-54798
Shmueli, G. and Lichtendahl Jr, K.C., 2017. Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.
Shmueli, G., Patel, N.R. and Bruce, P.C., 2016. Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner. John Wiley & Sons.
Tasioulas J. ‘Big Data, Human Rights and the Ethics of Scientific Research’ (December 1, 2016) abc.net.au https://www.abc.net.au/religion/articles/2016/11/30/4584324.ht