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Rules for Improving Efficiency of Data Analysis and Mining

The organisation forms some rules for improving the efficiency of data analysis tool and data mining. Companies to improve the decision making process and help managers take adequate steps to increase the overall growth use these mining tools. Data analysis or techniques could be considered similar to data mining as it helps in improving organisations knowledge related to customer trend and identify industrial patterns Banks are mainly forced not to disclose any kind of personal data of the customers to other companies like insurance or loan providing companies to increase their profitability. With the help of accurate data companies are mainly able to derive or conduct adequate analysis and data mining, which help the management to get the  right answers and influence the decision making process. This integrated operating system allows companies to minimise the risk of potential threats, which might arise from external forces

Data Mining And Data Analysis tool is an effective system, which is used by organisations to improve the overall decision-making capacity.  Moreover, organisation forms some rules for improving the efficiency of data analysis tool and data mining. Companies to improve the decision making process and help managers take adequate steps to increase the overall growth use these mining tools. Data analysis or techniques could be considered similar to data mining as it helps in improving organisations knowledge related to customer trend and identify industrial patterns (Bazeley & Jackson, 2013).

The role of contemporary organisations in conducting data mining and data analysis tools:

Currently with the help of data analysis tool and emerging technologies, companies are able to construct report on daily basis, which help operational employees to improve their functioning capacity. In addition, these functions allow organisation classify large set of data, which could help in depicting relationship and patterns. Data analysis tool not only help in evaluating historical data but also allows organisation to match, integrate, and prepare the data in such a way, which could help in enhancing their analysis.  Companies use these improved enhancements to derive precise results from new projects (Fernandes et al., 2015).  An example of banks could be evaluated, as it helps managers to reduce theft in their vicinity. Data analysis and data mining allow organisation such as banks to monitor their customer’s withdrawals and habits of operations.  This could eventually help in identifying any fraudulent transactions conducted on behalf of the clients, which could help in reducing frauds and increase customer satisfaction (Gelman, Carlin, Stern & Rubin, 2014).

Similarities between Data Analysis and Data Mining

The main analysis process starts with data collection, which involves an adequate information base forn future references. Companies mainly used effective scientific approach to recognise information, which is needed for the data mining process.  After collecting, the overall data from customers proper subset of tools of raw data is formed which help in moving all the data from one place to another. Holst, Sarbach, Tiglio & Vallisner (2016) stated that use of adequate Data Collection process allows organisation to improve their data source, which could help in supporting future decision making processes.

With the help of data mining and data analysis approaches companies are mainly able to depict data of their customers, as it helps in identifying their actual needs.  Companies mainly use more data to get adequate look in the trend and compare it with the old data. This process mainly allows the organisation to identify a trend, which could help in improving their competitive stance. The data mining process and analysis process takes up lot of time of the organisation, which increases the cost and negatively affects the overall profitability (Kolesnikov et al., 2015). Thus, companies outsource their data mining process to IT companies, which help in evaluating and analysing the data of customers. Majority of the IT companies are mainly helping different organisations to monitor the data and derive the actual output, which could help in their decision making process.

Ethical issues faced by organisations that use, store, collect and protect data of various customers in their information database. The companies are mainly faced with different ethical implications that allow them to take adequate steps, which could help in improving and securing the customer data. The ethical responsibilities mainly start from the instance when the company collects customer data after, which the whole responsibility for securing the data is up to the organisation. In this context, Ormondroyd et al., (2014) stated that companies use an integrated operating system, which could only be accessed by company’s employees.  

The first implication, which is faced by the company, is the collection of information from customers, where company needs to obtain corrects and adequate information from the customers.  This collection process mainly starts from any purchases, which is been conducted by the customer.  After the purchases, the customer information is relatively stored and adequately processed into the data storage.  Companies are forced not to use or ask secret questions from customers, which are not related to the data mining process (Powell, 2015).

Role of Data Analysis and Emerging Technologies in Daily Reports

The second ethical implication, which needs to be maintained by the organisation, is the access of collected data.  The data, which is been collected by the customer’s needs to be protected from any unethical use conducted by an individual or group. Thus, the company needs to implement relative security measures, which barred unauthorised person to gain access in the data storage.  However, relevant security easement should also be there, which could allow engineers and data analysis to access the data in time of need. The data collected by the company must be stored securely and efficiently with proper measures need to be taken for reducing any misplacement or manipulation. Thus, the company needs to keep the data in a central database located with adequate security, which could only be accessed by Limited authority. These actions good mainly help in reducing any kind of manipulation and help securing the information, which is collected from the customer (Rustici et al., 2013).

The third ethical implications, which needs to be maintained by the company the accuracy of the information of detection of misinformation from customers.  Companies are many forced to design an adequate method a technique that could help in gathering relevant and accurate data from the customers.  Without adequate method data, mining process could not provide the relevant outcome, which is needed by the organisation. Tasse, Bledsoe, Giepmans & Rahimzadeh (2016) stated that use of questionnaires and online social media platforms could help organisations to gather adequate data from customers, which would in turn help  in identifying adequate customer trends.

Conclusion:

Data mining and data analysis plays an effective role in analysing, evaluating and depicting better context data. With the help of accurate data companies are mainly able to derive or conduct adequate analysis and data mining, which help the management to get the  right answers and influence the decision making process. This integrated operating system allows companies to minimise the risk of potential threats, which might arise from external forces. Ott & Longnecker (2015) stated that according to privacy laws companies are barred from selling customer data to another personal without the prior consent of adequate regulatory authorities. Banks are mainly forced not to disclose any kind of personal data of the customers to other companies like insurance or loan providing companies to increase their profitability.

Reference:

Bazeley, P., & Jackson, K. (Eds.). (2013). Qualitative data analysis with NVivo. Sage Publications Limited.

Fernandes, S. M., Badano, L., Campos, A. G., Erdei, T., Mehdipoor, G., Hanboly, N., ...&Ribeiro, J. M. (2015). Poster session 2THE IMAGING EXAMINATIONP536Appropriate use criteria of transthoracic echocardiography and its clinical impact: a continuous challengeP537Implementation of proprietary plug-ins in the DICOM-based computerized echo reporting system fuels the use of 3D echo and deformation imaging in the clinical routine of a multivendor laboratoryP538Exercise stress echocardiography appropriate use criteria: real-life cases classification ease and agreement among cardiologistsANATOMY AND PHYSIOLOGY OF THE .... Eur Heart J Cardiovasc Imaging, 16(suppl 2), S73-S101.

Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2014). Bayesian data analysis (Vol. 2). Boca Raton, FL, USA: Chapman & Hall/CRC

Holst, M., Sarbach, O., Tiglio, M., &Vallisneri, M. (2016). The emergence of gravitational wave science: 100 years of development of mathematical theory, detectors, numerical algorithms, and data analysis tools. Bulletin of the American Mathematical Society, 53(4), 513-554.

Kolesnikov, N., Hastings, E., Keays, M., Melnichuk, O., Tang, Y. A., Williams, E., ...&Megy, K. (2015). ArrayExpress update—simplifying data submissions. Nucleic acids research, 43(D1), D1113-D1116.

Ormondroyd, E., Oates, S., Parker, M., Blair, E., & Watkins, H. (2014). Pre-symptomatic genetic testing for inherited cardiac conditions: a qualitative exploration of psychosocial and ethical implications. European Journal of Human Genetics, 22(1), 88-93.

Ott, R. L., &Longnecker, M. T. (2015). An introduction to statistical methods and data analysis. Nelson Education.

Powell, G. H. (2015). Coming together: The ethical implications of the one flesh union. Southeastern Baptist Theological Seminary.

Rustici, G., Kolesnikov, N., Brandizi, M., Burdett, T., Dylag, M., Emam, I., ... & Kurbatova, N. (2013). ArrayExpress update—trends in database growth and links to data analysis tools. Nucleic acids research, 41(D1), D987-D990.

Tassé, A. M., Bledsoe, M. J., Giepmans, L., & Rahimzadeh, V. (2016). Legal and Ethical Implications of Data Sharing in International Biobanking Research: Toward a Global Response.

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