1.
Figure 1: Flowchart of the firm
Data analysis and mining software are part of the operations. This software helps different departments of the organisation to take the necessary actions. This might be related to corporation information of the employees, performance of the employees, customers and analyze the necessary indicators. The practical use of data mining is to retrieve the data and analyze the data for the purpose of operations. In most of the cases, it is found that the analysis helps in categorizing different operational aspect of the company. The above-mentioned points are related to the satisfaction of the customers and reaching optimum revenue generation. In order to analyze the reports the data mining process is not only crucial but also serves as the ad hoc (Braha, 2013).
In case of complex data analysis or analysis based on longer period, it is important to follow some necessary steps. The initial steps of the process include collecting the data, assembling the data in a structured format. The data thus collected is used to interpret and formulate the results. This given a clear picture of the demand and the necessary steps that is to be taken by the organisation. Data analysis is important in many of the sectors. The most important use is in the financial sector. The banks and the financial organisation use the day-to-day data to collect the necessary information and to interpret them to conclude the profit. Similarly, in case of the e-commerce companies the data analysis is used for clickstream. This ensures the website visit by customers (Fan & Bifet, 2013).
Figure 2: Steps for data analysis
(Source: Fan & Bifet, 2013)
The data collected from the necessary sources, is used by, the data scientist to analyze the result. They then call the co-workers and decide a meeting for the same. Based on the data collected the company takes the strategies that would be beneficial in the near future. Like in an example, it might be cited that an e-commerce site is trying to interpret the result of page visitor. Then the data scientist would collect the data through data mining, this data would be interpreted and based on the data the operations and the marketing division of the company would take the obvious actions. However, the data collected from different source leads to analytics of data mining process. The subset of pulling the data is important to have a pool of large data and the refinement off the same. In this case the division of data is important to analyze different critical yet minor aspects of operations. These small aspects make it large and help in the managerial service of the company.
The ability of the operations of the company is fostered through the data and constructs multiple data reliability. The variances thus received helps in transformation of velocity, lifespan, dependency and perishability of set of data. The reliability of the extrinsic data is to collect the data and help in chain of strategies that the company can complexity in the future. The overall buying experience of the customers is enhanced through the process. However, there are some challenges that might be faced by the firms. This includes when the data shows a negative reliability. Then the company needs to recheck the validity of the collection of data and interpretation of the same. The growth and the prospect of the organisation is largely dependent on the collection of the data. In most of the cases, it is found that the companies recheck the data for a couple a times. On confirmation, the company used the data to integrate the necessary output. The IT department takes care of the data collection and mining, this is forwarded to the respective department. The companies come with the competitive strategies and differentiation strategies with the help of the data that is collected by the IT department. The responsibility of the IT department is to ensure that the data is interpreted from the raw data and the validity of the data is correct (Kolesnikov et al. 2015)
However, one of the ethical calls that the organisation is to have the data collected confirmed within themselves. The data collection process might make use of different information of the customers that might be personal. The huge responsibility of the company in this regard is to have the data safe and secured. Data could be retrieved through different unethical procedures that might hamper the face value of the company. In case of loss of data or data being hacked, the company has to make the problem public. Legal steps can be taken against the company. The company needs to abide by the legal steps that are crucial in such cases. Hence, the responsibility of the security of data is massive.
The next ethical consideration of the company is related to the lives or choices of the customer. The service that is mostly opted by the customers has to be ensured. In such case, it is the responsibility of the company to offer such products to the customers. Central database is pivotal in case of the accessibility of the data. The data is stored in a central database system. The security of the database has to be strong (Newcomer, Hatry &Wholey, 2015).
Conclusion
Companies need to ensure that they extract the right data, use it to help in the decision making of the customers. In most of the cases, the company tries to extract the data from different periods to understand the changing demands of the customers. Hence, it could be inferred that data analysis is an integral part of any company. The clear picture thus obtained helps in the profit maximization of the organization
References
Fan, W. & Bifet, A., (2013). Mining big data: current status, and forecast to the future. ACM sIGKDD Explorations Newsletter, 14(2), pp.1-5.
Gandhi, N., & Armstrong, L. J. (2016, October). Rice crop yield forecasting of tropical wet and dry climatic zone of India using data mining techniques. In Advances in Computer Applications (ICACA), IEEE International Conference on (pp. 357-363). IEEE.
Jain, N., & Srivastava, V. (2013). Data mining techniques: a survey paper. IJRET: International Journal of Research in Engineering and Technology, 2(11), 2319-1163.
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.
Newcomer, K. E., Hatry, H. P., &Wholey, J. S. (2015). Handbook of practical program evaluation. John Wiley & Sons.
Ott, R. L., &Longnecker, M. T. (2015). An introduction to statistical methods and data analysis. Nelson Education.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
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