Data Management After studying this week’s assigned readings (Attached PPT), discussion the following: 1. What are the business costs or risks of poor data quality?Support your discussion with at leas

Discussion 3

by Sheba Reddy Kota - Saturday, May 16, 2020, 8:25 AM

Risk occurred in businesses for weak data quality

Every business organization depends on customer feedback and satisfaction. Recently, maximum organizations focus on technical data services and their effectiveness. Lots of data have been published for customer feedback and improve their business (Keller, Korkmaz, Orr, Schroeder & Shipp, 2017). Technical data are available in details and it reduces costs of the business organizations. Sometimes by mistake, the data available in the sites of the company create bad feedback among the customers, because of the lack of information and its quality. Available data also create a poor background and it enhances the business risk. During operations, some employees have done this type of mistake and it increases the loss of the business (Kees, Berry, Burton & Sheehan, 2017). Weak data quality decreases the business operation and increases the potential destruction of the business. Sometimes lack of system quality data cannot proceed furtherly and the previous data existed in the sites. Customers always need updated data and present conditions of the organization, as technical systems running through the various traffics, so it can be said that due to technical fault this incident occurs (Casado-Vara, de la Prieta, Prieto & Corchado, 2018). It negatively affected customer service and decreases the goodwill of the business. Competitors improve to take advantage of the poor data and the possibility of destruction of the business comes true in the future.

Data mining meaning

Maximum technological aspects correlated with the excavation of data because without data the sites and Software cannot perform. Data mining is nothing but a system of patterns establishing from a data set and collecting the information from a data set and transform the data into a specific understandable mode (Ramírez-Gallego, Krawczyk, García, Woźniak & Herrera, 2017). Collecting the main information is the absolute aim of this process and establishing the quality of data is the target of the process. During data mining, many patterns are established for the processing of data and its effectiveness. The technical data just have the core information about a specific subject and it increases the level of the data (Pietruczuk, Rutkowski, Jaworski & Duda, 2017). Organizations always focus on core information, which is very effective for customer satisfaction. Information is the main element of the analysis, but it never tells about a data set. This process can support to improve the organizational benefits, the process of evaluating, and the present structure of the organization. This process is correlated with the statistics, learning of machines, and the systems of the database (Gautam, Singh & Shaikh, 2017). This process is nothing but a system of exploring the data from the large data set automatically. Therefore, it can be said that with the help of data mining lots of organizations improve their analysis power and enhances the quality of the data.

Meaning of text mining

Text mining is nothing but a process by which the organizations analyze the text data and make it understandable from different aspects. During the analysis, this process helps to understand the value of the text data and enhances the quality of the information (Niemann, Moehrle & Frischkorn, 2017). Gathering information from different text data is the main goal of this process. It is very helpful for the organizations to make the data very strategically and its information enhances the level of the activity. Making of resolution is the main target of this process because this process helps to identify the potential development by the utilization of the information (Rani & Kamal, 2018). This process also helps to understand the core information value and its effectiveness. Lots of companies gathered much information from their resources and transform into a specific set of data. During the organizational requirement, this information is very helpful to determine the actual benefit program of the business. This process also enhances data quality and improve organizational behavior by utilizing the major areas of business (Salloum, Al-Emran, Monem & Shaalan, 2018). Strategically exploring the information for the development of the organizational scenario is the main aspect of the process of text excavation. Therefore, it can be said that through this process every organization benefited and enhances the analysis of data.

References

Casado-Vara, R., de la Prieta, F., Prieto, J., & Corchado, J. M. (2018). Blockchain framework for IoT data quality via edge computing. In Proceedings of the 1st Workshop on Blockchain-enabled Networked Sensor Systems (pp. 19-24). 

Gautam, P., Singh, Y. P., & Shaikh, P. (2017). Significance and Importance of Data Mining for Marketing Analysis in Finance, Banking Sectors. Int. J. Appl. Res. Sci. Eng, 26-29.

Kees, J., Berry, C., Burton, S., & Sheehan, K. (2017). An analysis of data quality: Professional panels, student subject pools, and Amazon's Mechanical Turk. Journal of Advertising, 46(1), 141-155.

Keller, S., Korkmaz, G., Orr, M., Schroeder, A., & Shipp, S. (2017). The evolution of data quality: Understanding the transdisciplinary origins of data quality concepts and approaches. Annual Review of Statistics and Its Application, 4, 85-108.

Niemann, H., Moehrle, M. G., & Frischkorn, J. (2017). Use of a new patent text-mining and visualization method for identifying patenting patterns over time: Concept, method and test application. Technological Forecasting and Social Change, 115, 210-220.

Pietruczuk, L., Rutkowski, L., Jaworski, M., & Duda, P. (2017). How to adjust an ensemble size in stream data mining?. Information Sciences, 381, 46-54.

Ramírez-Gallego, S., Krawczyk, B., García, S., Woźniak, M., & Herrera, F. (2017). A survey on data preprocessing for data stream mining: Current status and future directions. Neurocomputing, 239, 39-57.

Rani, A. B., & Kamal, A. N. B. (2018). Text Mining to Concept Mining: Leads Feature Location in Software System. In 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) (pp. 1-7). IEEE.

Salloum, S. A., Al-Emran, M., Monem, A. A., & Shaalan, K. (2018). Using text mining techniques for extracting information from research articles. In Intelligent natural language processing: Trends and Applications (pp. 373-397). Springer, Cham.