Data Mining for Business Applications -myassignmenthelp.com

Question: Discuss about theData Mining for Business Applications. Answer: Introduction The need to collect more data from people that end up being personal information is prevalent in todays generation (The conversation, 2016). Such information is later analyzed and stored using super computers as magnanimous sets of data known as big data and at times may end up falling into the wrong hands making the owners of such information vulnerable to abuse and insecurity for that matter. However, big data has huge benefits as it has the power to revolutionize and transform the lives of people due to its predicting power (The conversation, 2016). It would feel great where someone knows what the weather will be with utmost accuracy like 24 hours before the actual event occurs (The conversation, 2016). However, there many risks and threats that may arise in the use of big data as due to the possibility of being utilized maliciously, more so for individuals who are ardent users of the internet and as such spend much time browsing and surfing the internet. Analysis Major security issues The stakes are getting bigger due to a large number of individuals engaged in security incidents that involve big data. In 2014 alone saw Arkansas University development system got breached and saw more than 50000 people fell victims as a result of the breach (The conversation, 2016).50000 people were a large number but It would more devastating if it was extended to more than 100 million people where email addresses, birth dates among other things were stored fell into the wrong hands or got stolen at eBay at the same time. According to professional experts insecurity issues, it is more task daunting trying to protect big sets of data. The reason behind such an explanation is the fact that nature of the technology used in storing and processing such data is complex and sophisticated. Companies such as Amazon that use and depend on distributed computing which entails dispersion of data centers geographically across the globe may be vulnerable to security risks(The conversation, 2016). Amazon as its global operations distributed in twelve regions and each of these regions as a data center making such centers susceptible to physical attacks as well as consistent cyber-attacks targeting servers in tens of thousands. It has always been pointed out by IT experts that a single point of access is important in ensuring security measures are guaranteed and is also one of the strategies employed in ensuring information is controlled(The conversation, 2016).It also argued that it is easier when one access point is made as opposed to having other hundred access points. On the contrary, big data is seen to operate in the opposite direction to the above-stated principle of a single access point has it has numerous access points distributed geographically(The conversation, 2016). The susceptibility of big data spreads far beyond because of its big size and the many access points in various locations. Also, another challenge originates from software components that are complex ignoring security issues, and that entails infrastructure from big data companies and as such leads to open positions that lead to attacks(The conversation, 2016).A good example is the Hadoop, a software which is a collection of components that allows programmers that are carrying massive amounts of data process such information using a computing framework. When Hadoop was being launched, it had features that were considered as basic that could support just a few individuals(The conversation, 2016). Many companies, however, have continued to use Hadoop in their data platform despite the fact that Hadoop was only built with the intention of supporting small scale operations. Ethical implications in data mining It is the duty and the role of consumers to ensure that they raise concerns and demands for a heightened level of data protection by having signed agreements and terms and conditions from institutions collecting and using data such as big data(The conversation, 2016). The use of encryptions, systems of intrusion detection and incorporation of auditing and control of access are some of the countermeasures employed to curb and prevent information from getting breached and falling into the wrong hands. Such security measures help in ensuring and promoting privacy of many clients. Despite the fact that heightened security is preferred in ensuring security measures are enhanced, it has been found that it can hurt peoples privacy as it creates legitimate reasons for collecting private information(Yu Zhang, 2008). Such information entails surfing the web for employees on computers at work places. It has been found out that when security agencies are involved in the process of collecting information while trying to boost security all the stakeholders are branded as terrorists or criminals and the information collected may be used to build cases against them. Although authorities possess and have knowledge about people, they could instruct companies such as Apple and Google to give them more information and intelligence such as versions of data that are decrypted. Also, issues such as the terms used in search engines and the online buying habits could be provided by such companies to the authorities. One of the major principles employed in justifying such kind of surveillances is that no one can be trusted. According to such principles, they have been found affordable and also more feasible as result of big data using such formidable technologies(Soares Ghani, 2010). Surprisingly, it is evident that after collection of such information, it joins the rest of the data leaving and creating room for future breaches as portrayed in snooping cases at national level. Another privacy issue of concern is that big data companies track every move of consumers aiming to establish targeted advertising. As such tracking becomes cheap and easily analyzed. The personality Insights from IBM could build a profile of the customer extending beyond demographics information. Conclusion However, data when used properly it can enhance efficiency in privacy by giving more information that gives better quality in terms of preventing cyber-attacks(The conversation, 2016). For instance in real world people do not have to worry about phishing since big data analytics can detect such mails as malicious. References Soares, C., Ghani, R. (2010). Data mining for business applications. Amsterdam: IOS. The conversation. (2016). Big data security problems threaten consumers' privacy. Retrieved August 19, 2017, from The Conversation: https://theconversation.com Yu, P., Zhang, C. (2008). Data mining for Business Applications. Dordecht: Springer.