ITC516 Data Mining and Visualisation for Business Intelligence

Answer

Task 1

1. Why Data mining is used in business

a. State the importance of data mining 

The data mining techniques can definitely enhance the large chunks of data that are available for companies. There are also numerous tools which are used by respective companies to implement the data mining techniques in their premises (Berry & Linoff, 2013).  Data mining involves the acquisition of relevant information that enables a company to make critical decisions that can lead to a company’s success.

b. How businesses could use data mining

The companies apply the data mining techniques for agile and efficient business operations. Data mining is applied in the business area like Retail, Banking and insurance.

In the retail industry data mining helps in identifying customer shopping behaviour, customer retention techniques, analyse customer groups and accomplice each and every group to the convenient group (Piatetsky-Shapiro, 2016).

Data Mining is basically used in the banking industry for optimising stock, cross selling and cash management. In the banking section data mining helps in distinguishing customer payment records, data mining helps to predict the customers’ profit, it also helps to identify the customers’ shopping patterns (Berry & Linoff, 2013).

Data mining is used to interact policy designing and policy selection. Thus, in the insurance sector, data mining assists to identify the risk factors, customers’ behaviour patterns, establish designs to detect any fraud. It also assists to analyse the factors to retain the customers for the company.

Data mining is used in health care industry to predict best medical practices, to accumulate all the details of the patients.

c. Discuss the benefits of using data mining

Data mining can identify the customers’ behaviour, their shopping patterns; it can also predict the customer retention methods. The companies via data mining can detect fraud. Therefore, these are the advantage that the companies can get via data mining (Mining, 2016).

2. Recent article related to data mining in business- “State constraints on ‘data mining’ and sends miner a notice”

Introduction

DMP's online systems have been established by the Australian government to provide information of the tenements free of charge; however the companies like Rio Tinto, Fortescue Metals Group are securing tenements and picking up new ground by using data mining techniques (Burrell & Burrell, 2017). This article will highlight that the companies are using data mining techniques for their benefits and to stay ahead of their competitors.

Body

WA Mines Minister Bill Marmion mentions that the government has decided to establish an online system and from now on the information of the tenements will be available free of charge on the website.

Several mining tenements are capitulated in Western Australia and are enrooted with the Department of Mines and Petroleum’s electronic system (Burrell & Burrell, 2017). The Department of Mines and Petroleum’s electronic system make the information public to everyone.

However, some companies have gained the backdoor access to approach the data on the system, they interrupt it and analyse it for any availability of tenant and DMP is not aware of that.

Now DMP has identified the companies- Rio Tinto, Fortescue Metals Group who have secured the tenements for others and send warnings as they are involved largely in data mining. Though the companies have refused the claim, however, the DMP's online system holds the evidence that these companies have recovered tenements within few minutes of the companies being surrendered (Burrell & Burrell, 2017).

Mr Marmion mentioned that a number of miners have raised concern over the continuing use of software of some big companies to trouble the DMP's online systems to gain a favour in protecting tenements. As the new system evolved, the information about tenements can be easily available via DMP’s website.

Conclusion

It can be concluded from the above discourse that the companies like Rio Tinto, Fortescue Metals Group are protecting tenements and picking up new ground by using data mining techniques and they are warned by DMP. This article also showcases that the companies are using data mining techniques for their benefits and to stay ahead of their opponents.

References

Berry, M. J., & Linoff, G. (2013). Data mining techniques: for marketing, sales, and customer support. John Wiley & Sons, Inc.

Burrell, A., & Burrell, A. (2017). Miners warned over high-speed land grab. Theaustralian.com.au. Retrieved 4 August 2017, from https://www.theaustralian.com.au/business/mining-energy/wa-cracks-down-on-tenement-data-mining-by-big-companies/newsstory/e90933b2e2c011077e92cfe8ba66a294.

Mining, W. I. D. (2016). Data Mining: Concepts and Techniques. Morgan Kaufinann.

Piatetsky-Shapiro, G. (2016). Advances in knowledge discovery and data mining (Vol. 21). U. M. Fayyad, P. Smyth, & R. Uthurusamy (Eds.). Menlo Park: AAAI press.

Task 2

Analysis of Security, Privacy and Ethical issues and implications in data mining

Introduction

The data mining technique is presenting significant security, privacy and ethical issues that must be noticed and based on that the companies must connote proper strategies to counter those issues.

This report will showcase all the issues related to security and privacy and also mentions the process via which the issues can be solved.

Major security issues in data mining

Data mining security is analogous to data mining privacy. The data warehouse is used to store the users’ personal data, so the data warehouse must be secured. Data are collected from various sources and are accumulated in the warehouse (Estivill-Castro & Brankovic, 2012).

To secure the database the companies need to install applications on their systems that are capable to secure the database from external threats and from intruders, it will also allow the employees to explore the data smoothly via data mining technique (Roddick & Lees, 2013). Again, the database must be made in a way such that only the authorised persons can have the access. The measures that the companies can take to prevent unauthorised access are as follows-

Limit access to the personal information: The companies by controlling the data and preventing all the employees from unauthorised access can protect and secure the personal data of the customers.

Anonymization: The duplicate data must be filtered out and a unique data set must be maintained.

Optimising the database:  By optimising the database, the authorised members of the companies can find the required data and queries fast and effectively.

Cryptography techniques: The data from the database can be secured by encrypting the data using the cryptographic technique. This will hide the data from the unauthorised users (Christensen, 2014).

Data alteration: The authorised users have the option to alter the data in the database this will hide the data of the database from unauthorised access, thus the database can be secured.

In this way, the database can be secured from unauthorised access and thus the customers’ personal information can be secured from mishandling and misuse of data mining technique.

Privacy issues in data mining

One can protect privacy by hindering one’s identity. The best approach can be taken by restricting the accessibility of the personal data. If someone finds that the information found is not appropriate, one can claim that their privacy has been compromised. However, if any company decides that they will use the data mining technique, then they must follow particular guidelines (Broder, 2013). The guidelines must be made clear and must be followed properly so that the users can be assured that their privacy has not been violated.

Huge chunks of data are regularly accumulated and analysed by data mining tools and marketing applications, in which the customers’ personal data are collected and they remain completely behind the scene (Chen et al., 2015). The privacy advocates oppose this kind of data usage; however, the privacy advocates fail to stop the usage of data mining technique of the companies.

This data mining technique is used in scientific research, climate-shift research. The data mining technique implementation also helps in the areas where privacy issues are linked to the well-being of the users. The data mining techniques can be used to address those who want to evade tax; the data mining technique also helps in criminal investigation and identifying fraud (Christensen, 2014). This technique also has a useful contribution to medical science and health care. Therefore, data mining technique is beneficial; however, the users will have to maintain privacy and must be aware of how their personal data is being used by the companies for data mining technique ("Big data security problems threaten consumers' privacy", 2017). Both the companies and the companies will have to look after the fact that the users' privacy is not compromised at any cost. The users have the right to take a proactive assertive approach to protect their privacy and if required have the right to communicate with the data holder to apply constraints wherever the users feel appropriate.

Ethical implications in data mining

The companies face an ethical dilemma whether they should take customers’ opinion or not while they try to store the personal information of the customers in their database. The companies if take opinions from the customers to store data of the customers, the customers may hesitate, thus this hesitation may hurt its competitive edge in the market. The companies who are storing the customers' data must act responsibly, otherwise, the, if the data gets hacked or misused, the company, will lose the customers' trust and can even get into trouble (Agrawal & Srikant, 2012). Therefore, the companies who want to use the data mining techniques to enhance the business activities must act responsibly, they must be aware of all the ethical issues associated with it and so, they should not misuse the data.

The data mining can segregate people on the basis of sexual, racial and religious contexts. This type of practice of data mining is both unethical and illegal ("Big Data, Human Rights and the Ethics of Scientific Research – Opinion – ABC Religion & Ethics (Australian Broadcasting Corporation)", 2017). Therefore, the users must be aware of the facts on how their personal data should be stored in the database; so that the customers can be aware of the consequences they can face in future (Berry & Linoff, 2014). In this way, if the users' information is accessed in future, the users can know where their data is being used.

Thus the ethical concerns in data mining can be viewed as two primary ethical contexts and can be related to individualism and solitude. The importance of individualism and solitude must be taken into consideration and must be valued to make sure every user gets treated reasonably (Bigus, 2013). The companies must be aware of the ethical concerns related to data mining, so, they must implement the data mining technique in an efficient way to make sure their customers’ personal data not being compromised.

Importance of these implications in data mining

In the retail section data mining helps in identifying customer shopping behaviour, customer retention techniques, analyse customer groups and accomplice each and every group to the convenient group. The retailers must be aware of the security, privacy and ethical issues if the customer information gets compromised; it will affect their business and will also affect company’s reputation (Broder, 2013). The retailers should be careful when dealing with the bank cards while dealing with the money transaction that is why they implement special security and privacy solutions in their respective premises.

Conclusion

It can be concluded from the above discourse that the ethical, security and privacy issues have been discussed elaborately in this report and the probable solutions have also been discussed. This article also showcases the applications of data mining in business. Data mining is used wisely in retailing, banking, health care and nearly every industry. That is why it is the responsibility of the individual industries to cope up with the security and privacy issues and must implement a proper data mining strategy to make sure they can be successful in their venture not compromising the users' personal data.

References

Agrawal, R. & Srikant, R. (2012), Privacy-preserving data mining, in W. Chen, J. Naughton & P. A. Bernstein, eds, ‘ACM SIGMOD Conference on the Management of Data’, ACM, Dallas, TX, pp. 439– 450.

Berry, M. & Linoff, G. (2014), Data Mining Techniques — for Marketing, Sales and Customer Support, John Wiley & Sons, NY, USA.

Big data security problems threaten consumers' privacy. (2017). The Conversation. Retrieved 5 August 2017, from https://theconversation.com/big-data-security-problems-threaten-consumers-privacy-54798

Big Data, Human Rights and the Ethics of Scientific Research – Opinion – ABC Religion & Ethics (Australian Broadcasting Corporation). (2017). Abc.net.au. Retrieved 5 August 2017, from https://www.abc.net.au/religion/articles/2016/11/30/4584324.htm

Bigus, J. (2013), Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support, McGraw-Hill, NY.

Broder, A. (2013), Data mining, the internet, and privacy, in B. Masand & M. Spiliopoulou, eds, ‘International Workshop WEBKDD ’99; Web usage analysis and user profiling’, Springer Verlag LNCS 2000, SanDiego, CA, pp. 56–73.

Chen, M.-S., Han, J. & Yu, Phillip, S. (2015), ‘Data mining: an overview from database perspective’, IEEE Trans. Knowledge and Data Engineering 8(6), 866–883.

Christensen, C. M. (2014), The innovator’s dilemma: when new technologies cause great firms to fail, Harvard Business School Press.

Estivill-Castro, V. & Brankovic, L. (2012), Data swapping: Balancing privacy against precision in mining for logic rules, in M. Mohania & A. Tjoa, eds, ‘Data Warehousing and Knowledge Discovery DaWaK-99’, Springer-Verlag LNCS 1676, Florence, Italy, pp. 389–398.

Roddick, J. F. & Lees, B. G. (2013), Paradigms for spatial and spatio-temporal data mining, in H. Miller & J. Han, eds, ‘Geographic Data Mining and Knowledge Discovery’, Research Monographs in Geographic Information Systems, Taylor and Francis, London, pp. 33–49.