The Issue of Data Mining Free-Samples for Students-Myassignmenthelp

Question: Explains the role of Data Analysis tools and data Mining in Contemporary Organisations Identifies and Explains the Ethical Implications around gathering, storing and using Customer Information. Answer: Introduction Linoff, Berry, (2011) describe data mining (at times referred to as data or knowledge innovation) as the procedure of examining data from different viewpoints and summarizing it into expedient information one which can be employed to upsurge income, cut expenditures, or even both. Data mining software is among the common methodical tools used by analysts to analyze data. It enables operators to scrutinize data from various angles or dimensions, classify it, and summarize the identified relationship. Precisely, data mining is the process executed when finding correspondences or patterns amongst multitudes of fields in enormous relational databanks. Companies need to keep hold of a broad assortment of information on the business itself, clienteles, and staff members among other stakeholders. It is important to make sure that this information is well guarded and as safe as possible. Every organization holding its customer information should also be certain that it is complying with th e terms laid out by the responsible statutes such as theData Protection Act 1998. Methodology/ Design/ Approach Various scholars and authors have expressed their sentiments regarding the issue of ethical data mining. This research paper will extensively use their theoretical works to expound on this particular subject. Various hypothetical models, regulations and legislation from a broad array of jurisdictions as well as a collection of foundational philosophies were referred to while developing the theme of the study. This assemblage of foundational ideologies was assessed and refined through incorporation of respondents perceptions and the integration of moral as well as ethical dictums. These sources were methodically evaluated to expand on their contents with a primary aim of identifying the research gaps. Findings According to the viewpoints of various writers whose works were reviewed for this project, the importance of data analysis cannot be underestimated. In other words, no company can thrive in the current competitive business environment without analyzing the available data. Hellemans, et.al. (2007). support that efficient data analysis offers various benefits such as ruling out the human prejudice especially through appropriate statistical treatment. They add that these technical processes enable accountants and other specialists to break a macro picture of raw data into a macro one and thereafter obtain the meaningful comprehensions from the dataset. These arguments are sufficiently suggestive to conclude that data analysis is the helping hand of any corporation in the contemporary word. Whether an individual intends to arrive at some procedural decisions such as fine-tune innovative product launch tactic, data analysis as Hellemans, et.al. (2007). point out is the key to all the prob lems. However, it is worth noting that data analysis is accomplished through the use of various information technology tools such as Solver, WolframAlpha, Google Search Operators, NodeXL, RapidMiner, and KNIME among others. These IT tools enable organization members to manipulate, assess, and model data in an incredible manner for easier understanding and usage. As Banker, Charnes, Cooper, (2014) maintain, such analysis tools allow administrators to acquire huge quantities of searched data and analyze it to realize expedient patterns or correlations, which are then applied when predicting future behavior. In his work, York, Rosa, Dietz, (2003) argue that IT analytic tools continue to develop and ease the operations of every business entity regardless of its size with the overall objective of refining Business Intelligence (BI), augmenting decision investigation, and, more lately, endorsing connections with Business Process Management (BPM), also referred to as workflow. At the inception of his work, Witten, Frank, Hall, Pal, (2016)define data mining as the process of taking out data, examining it from different perspectives or dimensions, and then after generating a summary of the information in a valuable manner which categorizes relationships within the data.Ideally, there exist two forms of data mining, namely, descriptive, that provides information regarding present data; and predictive, the one which crafts predictions based on the available data. Dunham, (2006) suggests that a company data warehouse or a sub-divisional data store is inoperable especially if that data can hardly be subjected to work. One among the principal objectives of every analytic tool such as the ones discussed beforehand is to improve procedures which can easily be employed by average professionals in their occupations, instead of necessitating for advanced statistical familiarity or knowledge. Simultaneously, the data information and warehouse acquired from data mining and examination, requires being well-matched across a broad range of systems. Kasemsap, (2015) in his research paper expounds further on the role of data mining in business. According to his line of argument, data mining is mainly used at the present by organizations deemed to have a robust customer emphasis communication, financial, retail as well as marketing firms. It allows such corporations to define key affiliations amongst "internal" factors for example staff skills, product, positioning, or price, and "external" aspects such as competition, customer demographics, and economic indicators. Ultimately, it allows these companies to "drill down" into detailed summary information in order to view precise transactional data. Nevertheless, majority of the theorists do not culminate their arguments without mentioning the ethical implications around gathering, storing and using customer information. They maintain that it is imperative to ensure that stakeholders information is well protected and as safe as possible. Discussion As hinted in the previous section of this paper, the data gathering, analysis, and storing procedures ought to ascertain the safety of critical business information. Various individuals, associations, and regulation bodies in their materials acknowledge the significance of ethical practices while managing or handing data on consumers. Ferretti, (2014), for instance provides several ideologies of data protection. He sources eight principles from the Information Commissioners Office and according to him any existing corporation processing data should conform to them. Customer information must be honestly and legitimately processed It is should be used for limited purposes It ought to be satisfactory, applicable, and not excessive It should be accurate It should be kept longer than necessary It should be processed in line with the data subjects (for instance the customer) rights It should be secure It should not be transmitted to nations without sufficient protection In addition to the above guidelines, corporations must recognize the category of information they want to store and the reason behind the stowage. Lyon, (2003) emphasizes that they should be clear as to the nature of information they intend to keep on customers or prospective customers and why, For example any personal details such as name or address. This takes in information gathered by electronic means, for instance from e-commerce dealings. In other words, they must ensure that they take the information protection principles into consideration while stowing customer data. In addition, risk evaluation ought to be conducted to make sure that security schemes are in place to safeguard data. Organizations should improve confidentiality measures to uphold data security. For instance as Cohen, (2009) says it might be decided not to convey customer particulars over the phone; part of the safety scheme would be in making certain that all staff members are aware of this particular policy. In connection to this principle, it is essential for a corporation to institute a recovery system to obtain stored information. Tidying away or archiving all of the company documentation and records might be time consuming and hence make recovery process problematic. As such, it is important for them to have schemes in place to manage information stowing and repossession. They should make sure there is minimal reduplication of client statistics between for instance the customer database and accounts system. This aspects aid in managing the customer facts and conform to data p rotection decree. Conclusion Today, in the contemporary business world, more individuals are employed to collect, handle and distribute information than in any other job. Lots of computers reside the earth and numerous oodles of miles air waves, optical fiber, and wire connect individuals, their workstations as well as the immense collection of information handling expedients together. Indeed, the current the social order is undeniably an information society; an information age. The question before everybody now is whether ethical or moral issues are observed while handling this vast array of data. This is a question that ought to especially concern those corporations gathering, processing, and storing voluminous amounts of data detailing personal intricacies of their clientless. It is evident from the discussions in this paper that majority of the skirmishes encountered between organizations and the stakeholder stem from the manner the former handles the information of the latter. Therefore, it is significant f or them to observe the foremost concerns of information ethics during this information era. References Top of Form Banker, R. D., Charnes, A., Cooper, W. W. (2014). Some models for estimating technical and scale inefficiencies in data envelopment analysis.Management science,30(9), 1078-1092. Top of Form Cohen, J. E. (2009). Privacy, ideology, and technology: A response to Jeffrey Rosen.Geo. LJ,89, 2029.Bottom of Form Dunham, M. H. (2006).Data mining: Introductory and advanced topics. Pearson Education India. Ferretti, F. (2014).EU competition law, the consumer interest and data protection: The exchange of consumer information in the retail financial sector. Hellemans, J., Mortier, G., De Paepe, A., Speleman, F., Vandesompele, J. (2007). qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data.Genome biology,8(2), R19. Kasemsap, K. (2015). The role of data mining for business intelligence in knowledge management.Integration of data mining in business intelligence systems, 12-33. Linoff, G., Berry, M. J. A. (2011).Data mining techniques: For marketing, sales, and customer relationship management. Indianapolis, IN: Wiley Pub. Lyon, D. (2003).Surveillance as social sorting: Privacy, risk, and digital discrimination. Psychology Press. Witten, I. H., Frank, E., Hall, M. A., Pal, C. J. (2016).Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann. York, R., Rosa, E. A., Dietz, T. (2003). STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts.Ecological economics,46(3), 351-365. Bottom of Form