Use of data mining in business

The data mining is the method used for applying sorting on the large data set for identifying the pattern and relationship between them to solve a related problem. The future trends of the business can be predicted by using data mining tools. It is commonly applied on the large scale data for creating information processing with the help of methods like data collection, warehousing, extraction, analysing, and statistics. It is applied on the application related to decision support system, machine learning, artificial intelligence, and business intelligence. The data mining tasks are semi-automatic in nature which identifies the pattern by cluster analysis, anomaly detection and dependencies. The data mining procedure depends on six major tasks which are categorised as Anomaly detection, association of rule learning, clustering of activities, classification of data, regression of the function, and summarization of the data set. The result of the data mining helps in predicting the future behaviour of the activities and the performance of the product.

The data mining is used for identifying the hidden patterns which helps in prediction to analyse the impact on businesses. It helps in achieving the goal and objective of the organization. It helps in predicting the future sales of the product and services of the organization. “The data mining tools helps in developing predictive modelling” (Maheshwari, 2015). The graphical user interface is used for analysing the data. It is used for segmenting the customers, analysis of the market, forecasting the sales of the organization, managing relationship with the customers, management of risks, and fraud detection.

The diversification should be built up for changing traditional Mongolian organizational infrastructure to the infrastructure with the specification of big data and cloud computing. “The Mongolia industries are working on developing strong electricity power supply” (Jie, 2017). The big data and cloud computing helps in developing application at lower price. It is working on developing agro processing enterprise which helps in increasing the agricultural products. Foreign trade and ecommerce is at boom for the inner Mogolia.

The data mining activities help in getting value added output which helps in increasing the sale of the products. It is working on developing largest data centre in North China by minging data from the all the sectors such as steel, agro-based, power, railways, and etc. The data mining methodologies helps in improving the economic development of the country by building risks models and fraud detection systems. The safety and quality issues associated with the enterprise can be resolved by using the concept of data mining. The operation of the organization can be improved by managing the relationship with the supply chain department. The country is working in the direction to sold the raw material at lowest price which helps in increasing the sale of inputs and products. The inclusion of foreign trade and technology of e-commerce brings a major change in the working tactics of Mongolian industries.

Business Requirement

“Data mining is the information technology which is used for handling various databases and large amount of data in the effective manner” (Apte, 2011). The data mining helps in predicting th logical pattern of the data set. With the application of the data mining methodology, there are some issues of security, privacy, and ethical implication associated with it.

The privacy, security, and ethical issues is the major concern areas for managing the big data on the distributed computing environment. “The big data infrastructure opens the path for the potential attacks” (Petre, 2013). The data mining can act as a solution to the problems encountered in managing the big data.

The table below helps in identifying the security, privacy, and ethical issues associated with the data mining dilemma.

Issues	Descriptions
Security Issues	The big data security is required for managing the sheer scale of the people. The security professionals works on protecting the big data sets. The problem of handling big data is associated with the distributed computing technology associated with the large scale industries such as Amazon. The working of the organization is divided into global operational areas for creating data centres which helps in protecting the organization from physical and cyber-attacks.
Privacy Issues	The management of privacy is the major issue with big data management. The physical spaces should be controlled from the accessing of information. The big data infrastructure opens the path for the potential attacks. Underutilization of the resources and data generally take place due to the privacy issues associated with the flow of information.
Ethical Issues	Ethical standards should be followed for pursuing public goods. The databases should not be hacked. The human should use the internet and associated information when he has a right to access the internet. The unauthorised user should not access the information.

The tools of data mining help in handling the data in the secured manner. “The use of Hadoop is the best alternative for managing the unauthorised accessing of data in the management of big data on the platform of distributed computing” (Roddick, 2016). The accuracy and quality of the information can be improved by managing the big data from potential attacks. The big data analytics solution helps in picking and tracking the occurrence of malicious emails. The early detection of fraud helps in providing promising results.

“The management of big data by using data mining tools helps in improving the user experience” (Paidi, 2012)). The tracking of big data is easier to analyse. For example, Insurance Company has to manage big data profile by involving questioning coverage, the problems can be solved by realistic options, and helps in fraud detection of the company. “The data Anonymization is the process which can be used for overcoming the problems and risks associated with the privacy issues” (Fule, 2015). The data can be accessed without authorisation when:

• It is based on data driven research which provides value and benefits to the society.
• The queries should not be generated by the data user. The excessive risks can be reduced
• The data and action should be full transparent to the data users.
• The discrimination activities should be punished.
• Compensation mechanism should be used for dealing with discrimination activities
• The potential gains should be estimated by the data users.
• The democracy can be achieved in decision making process by using the ethical standards in the working curriculum of the organization.

“The technology of data mining helps in dealing with large amount of data located at different databases” (Tasioulas, 2016). The data mining technology helps in creating new opportunities for automatic prediction of trends and related behaviour, automation in the discovery of unknown patterns, predictive model for artificial neural network, development of decision tree by using the technology of classification and regression tree and the Chi square automatic interaction detection, genetic algorithms are the basic methodology used for optimization techniques, k-nearest neighbour technique, and significant use of rule induction methods.

The methodology of data mining is used in creating process to fight against terrorism, development of bio-information for creating cure methods for diseases, development of web and semantic web by using the resource description framework, in the cluster analysis, prediction techniques, in the development of business intelligence system, and others. It is presently mainly applied in the field of healthcare sector, retail industry, finance, telecommunication, web mining, text mining, and higher education.

The data mining technology is going to be used in the near future in the areas of ubiquitous data mining, development of mining related with the hypertext and hypermedia resources, multimedia channels related to data mining, and others. The focus is also given on the areas related to geographic and spatial data mining, sequence and time series development, data mining based on constraints, and phenomenal based data mining. The data mining concept is used for developing social welfare program. “The human rights should be given privilege for determining the concept of data mining in the field of big data analytics” (Tasioulas, 2016).

Conclusion:

The risks and gains are associated with the use of big data in the working culture of the organization. The security is the major challenge which come forth in the effective utilization of the data. The minimization of the risks takes place by fixing the human rights and ethical standards for the changing condition and working requirements of the organization.

References:

Apte, C. (2011). The role of data mining in business optimization. Retrieved from https://www.siam.org/meetings/sdm11/apte.pdf

Fule, P. (2015). Detecting privacy and ethical sensitivity in data mining results. Retrieved from https://crpit.com/confpapers/CRPITV26Fule.pdf

Jie, Z. (2017). From mining to big data: Inner Mongolia’s economic development. Retrieved from https://www.chinadaily.com.cn/business/2017-08/08/content_30365352_2.htm

Maheshwari, S. (2015). Data mining concepts, application, and research direction. Retrieved from https://www.ijarcce.com/upload/2015/november-15/IJARCCE%2088.pdf

Paidi, A. (2012). Data mining future trends and applications. Retrieved from https://www.ijmer.com/papers/Vol2_Issue6/ES2646574663.pdf

Petre, R. (2013). Data mining solution to the business environment. Retrieved from https://www.dbjournal.ro/archive/14/14_3.pdf

Roddick, J. (2016). On the ethical and legal implications of data mining. Retrieved from https://csem.flinders.edu.au/research/techreps/SIE06001.pdf

Ryoo, J. (2016). Big data security problems threaten consumer privacy. Retrieved from https://theconversation.com/big-data-security-problems-threaten-consumers-privacy-54798

Tasioulas, J. (2016). Big data, human rights, and the ethics of scientific research. Retrieved from https://www.abc.net.au/religion/articles/2016/11/30/4584324.htm