Tuesday, May 5, 2020

Data Intensive Applications System †Free Samples to Students

Question: Discuss about the Data Intensive Applications System. Answer: Introduction The data mining and data analysis tools play an inevitable tool for the modern business organizations. Due to the complexity of the operations of the modern business organizations, the importance of implementations of data analysis tools is of utmost priority. The data analysis and data mining tools can be said to be useful in terms of storing and customization. The entire processes of data mining can be further divided into various steps. These are data integration, data storage, data modification and data extraction. The given report also highlights the merits and de-merits of data analysis process. Based on the given analysis, certain recommendations will also be given. The methodology of data mining and data analysis will highlight the importance of several statistical tools. There are several tools of data mining like Weca , which the organizations can use. On the other hand, there are several statistical tools like SPSS, R, stata which the modern organizations can use in order to analyze their data. It is of great essence for modern business organizations to store information about all their customers in order to provide them effective services. On the contrary, it has been seen that there are many customers who are not willing to share their private information. Due to this reason, it is of utmost importance for the organization to use the tool of data mining in order to store the information of their customers (Chen Zhang, 2014) Findings and Discussion The process of data mining involves classification of data that further helps to secure personal data and information of the customers. In the initial stages, the process of data mining was useful for the end users that are the management of the firm. However, several implementations have taken place in the data mining process. Due to this reason, the organizations can easily minimize different ad-hoc issues with the help of the process of data mining. The given process involves different classification of data from a vast pool of information that further helps in the decision making process of the organizations. In addition to this, it can be also inferred that if data mining process is implemented in an effective manner, then, different malpractices in the operations process can be reduced. In addition to this, it can be also inferred that the organization can reduce slack times in their operational process and thus help them to reduce operational costs as well. On the other hand, it can be also inferred that the organization can enhance its sustainability with their respective data mining and data analysis tools. This is mainly because; data mining helps to increase the service quality of then organizations in an effective manner. In addition to this, the process of data mining helps the organization to understand macro and micro business environment trends and demands of its products among the customers. Based on the analysis and findings, the organization can take appropriate steps that will further help them to meet their short-term and long-term goals (Provost Fawcett, 2013). Marz Warre (2015) opine that there are several ethical challenges that a business organization can face during the process of data mining and data storage. It is of utmost importance to store personal information of their customers in their respective database. However, it has been that many companies can utilize the personal information of the customers and thus can breach their ethical code of conduct. It is of utmost importance for the firm to protect the privacy information of their customers and do not breach their ethical code of conduct. The employers of the firm need to abide by the code of ethics and should also ensure that no private information is leaked to any third party. If the employees of a respective organization fail to abide the code of ethics then, it will be harmful for the image of the firm. On the other hand, another ethical consideration may arise. If any organization interprets and provides wrong data, the, then, it may cause error of falsification. This can harm the firm in a bad manner. In addition to this, it can be also inferred that the management of the organization needs to cross check all the data before they can publish it officially. The employees of the firm also need to ensure all proper steps of data validation is followed in an effective manner. If the organization is successful in maintaining their ethical considerations, then, the customers will feel secure that their private information is safe. Therefore, the process of data mining and data analysis has wide range of consequences for both the firms as well its customers (Provost Fawcett, 2013). In addition to this, it can be inferred that there are several merits of using data mining tools. These are in the form of data storage, data modification, data validation and analyzing the internal and external trends. Conclusion It can be concluded that the process of data mining and data analysis helps to built up the brand image of an organization. It can be also inferred that both these processes helps to increase the level of competitiveness among the firms within the same industry. On the other hand, it is important for the firm to gather and record information in an ethical manner. This will further help them to gain the confidence of their customers. Therefore, the modern firms need to realize the importance of data analysis tools and techniques in order to gain a competitive advantage in the market. References Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., Escobar, G. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients.Health Affairs,33(7), 1123-1131. Chen, C. P., Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data.Information Sciences,275, 314-347. Delen, D., Demirkan, H. (2013). Data, information and analytics as services. Demirkan, H., Delen, D. (2013). Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud.Decision Support Systems,55(1), 412-421 Marz, N., Warren, J. (2015).Big Data: Principles and best practices of scalable realtime data systems. Manning Publications Co.. Provost, F., Fawcett, T. (2013).Data Science for Business: What you need to know about data mining and data-analytic thinking. " O'Reilly Media, Inc.". Shmueli, G., Patel, N. R., Bruce, P. C. (2016).Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner. John Wiley Sons. Steinberg, G. B., Church, B. W., McCall, C. J., Scott, A. B., Kalis, B. P. (2014). Novel predictive models for metabolic syndrome risk: a" big data" analytic approach.The American journal of managed care,20(6), e221-8.

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