Response to 2 authors on the subject (150 words each)-MIS

The business costs and risks of poor data quality

Data is needed everywhere for the smooth running of an organization. The proper management of these data is also needed for the hassle-free operation of any organization (Turban, Volonino& Wood, 2015). But if the quality of data is poor it can impose negative consequences on the organization. If poor quality data is not recognized within a company it can have a negative social and economic impact on the organization (Choi & Luo, 2019). When the quality of data is poor, the satisfaction rate of the customers also decreases significantly which is quite harmful to any business firm. Poor data quality leads to poor decision making that leads an organization towards risks and impacts the company negatively (Côrte-Real, Ruivo& Oliveira, 2020). There is a strong connection between poor data quality and how it inflicts unnecessary costs and expenses within an organization. When poor quality data is not identified and worked upon then it leads to high production costs in an organization. For example, repeating a developmental project and making an inaccurate decision.

Explanation of data mining and its importance

Data mining is the practice followed by large organizations where a large amount of data or information is processed to turn it into useful information. This is done by identifying patterns in the data which can help in making effective strategies for marketing (Ghasemaghaei&Calic, 2019). Data mining is helpful for planning and decision making within an organization. With improved strategies for marketing using data mining, the sales of the organization will increase and decrease extra costs and expenses (Wang, Cao & Yu, 2020). Data mining helps to identify the meaningful patterns in a huge collection of raw data. This can be used in various ways in the organization like managing risks, filtering spam email, and marketing the database. Data mining also helps in the smooth management of a huge amount of data. This helps to improve the quality of data and hence is beneficial for the company (Novikov, 2019).

Discussion on text mining

Text mining is a technology that helps to convert texts into data and documents. This helps software or program to read the text converted data more easily (Chen, Tsangaratos, Ilia, Duan & Chen, 2019). This structured data is suitable for insightful analysis which is beneficial for the organization. This is because with the help of text mining data can be managed and handled smoothly (Sezgen, Mason & Mayer, 2019). Text mining helps to identify facts, assertions as well as various relationships that would otherwise have remained buried within the huge amount of texts. Text mining helps to extract useful information from texts and form structured data. This structured data can be analyzed further and presented in the form of charts, HTML tables, and mind maps. A variety of methods are used by text mining to interpret texts and form structured data (Galati &Bigliardi, 2019). The structured data that are formed from text mining are integrated within databases or dashboards of business intelligence. These are then used for various kinds of analytics such as descriptive, predictive, and prescriptive. Text mining is hence beneficial for business organizations as it enables them to extract data from texts and use the structured data for better analysis. This helps them to manage data more efficiently and effectively (Greco &Polli, 2020).

 

References:

Turban, E., Volonino, L., & Wood, G. R. (2015). Information technology for management: Digital strategies for insight, action, and sustainable performance. Wiley Publishing.

Choi, T. M., & Luo, S. (2019). Data quality challenges for sustainable fashion supply chain operations in emerging markets: Roles of blockchain, government sponsors and environment taxes. Transportation Research Part E: Logistics and Transportation Review, 131, 139-152.

Côrte-Real, N., Ruivo, P., & Oliveira, T. (2020). Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value?. Information & Management, 57(1), 103141.

Ghasemaghaei, M., &Calic, G. (2019). Can big data improve firm decision quality? The role of data quality and data diagnosticity. Decision Support Systems, 120, 38-49.

Wang, S., Cao, J., & Yu, P. (2020). Deep learning for spatio-temporal data mining: A survey. IEEE Transactions on Knowledge and Data Engineering.

Novikov, A. V. (2019). PyClustering: Data mining library. Journal of Open Source Software, 4(36), 1230.

Chen, W., Tsangaratos, P., Ilia, I., Duan, Z., & Chen, X. (2019). Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods. Science of The Total Environment, 684, 31-49.

Greco, F., &Polli, A. (2020). Emotional Text Mining: Customer profiling in brand management. International Journal of Information Management, 51, 101934.

Galati, F., &Bigliardi, B. (2019). Industry 4.0: Emerging themes and future research avenues using a text mining approach. Computers in Industry, 109, 100-113.

Sezgen, E., Mason, K. J., & Mayer, R. (2019). Voice of airline passenger: A text mining approach to understand customer satisfaction. Journal of Air Transport Management, 77, 65-74.