Response to 2 authors on the subject (150 words each)-MIS
Question of original discussion-
After studying this week’s assigned readings, discuss the following:
1. What are the business costs or risks of poor data quality? Support your discussion with at least 3 references.
2. What is data mining? Support your discussion with at least 3 references.
3. What is text mining? Support your discussion with at least 3 references.
Please use APA throughout.
Read and respond to at least two (2) of your classmates (attached below). In your response to your classmates, consider comparing your articles to those of your classmates. Below are additional suggestions on how to respond to your classmates’ discussions:
- Ask a probing question, substantiated with additional background information, evidence or research.
- Share an insight from having read your colleagues’ postings, synthesizing the information to provide new perspectives.
- Offer and support an alternative perspective using readings from the classroom or from your own research.
- Validate an idea with your own experience and additional research.
- Make a suggestion based on additional evidence drawn from readings or after synthesizing multiple postings.
- Expand on your colleagues’ postings by providing additional insights or contrasting perspectives based on readings and evidence.
150 words for each author. APA format.
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.
Business costs or risks associated with poor data quality
Poor quality data is unarguably one of the most common and huge problem for a lot of organizations. Some of the very concerning costs or risks associated with poor quality data are listed as below:
1. Missed opportunities: An organization might end up missing important opportunities for NPD (New Product Development) or a new customer need simply because missing or poor quality data. Eventually, a competitor or new entrant in market might make money off that opportunity. This will lead to lost revenue and ultimately the profit that the organization could not make.
2. Reputational damage: Poor quality data in any organization can lead several problems if the financial institutions do not have the most accurate information about its customers. Some times these can also lead to huge public relations disasters.
3. Decreased productivity: Bad data can cause employees to be slow because poor quality data would require more work to clean and structure or even to verify the accuracy of the source of such data while verifying their analysis. This in turn causes slow growth.
Data mining
Data mining is the process of extraction of data, analysis of it and then generation of useful information in form that can be used to make sound business decisions. This process is usually automatic searching large databases. The purpose of it is to identify patterns and trends that go beyond the conventional data analysis processes. Data mining can also be used in predictive modeling since it already uses mathematical algorithms that can extrapolate the data for future events. Hence, this can generate a lot of valuable information for organizations which can use it to make important and critical business decisions.
Text mining
Text mining is the process of examining large chunk of text data or several large collections of documents to retrieve new information. It identifies facts, assertions and relationships from massive amounts of texts. This process is also called Natural Language Processing. Also, it is all about processing unstructured data to produce structured results that can be integrated into the conventional databases to further analyze it as a whole. It is very similar to data mining but has the focus on non-numerical data i.e. text or unstructured data. That said, the very first step in text mining is to organize and structure that data so that it can undergo qualitative as well as quantitative analysis.
References:
Releases, F. (2017, May 31). Poor-Quality Data Imposes Costs and Risks on Businesses, Says New Forbes Insights Report. Retrieved November 03, 2020, from
https://www.forbes.com/sites/forbespr/2017/05/31/poor-quality-data-imposes-costs-and-risks-on-businesses-says-new-forbes-insights-report/?sh=40e2926b452b
By, A. (2020, April 01). The Costs of Poor Data Quality. Retrieved November 03, 2020, from
Bolander, J. (2019, July 26). How Poor Data Quality Negatively Impact Your Business. Retrieved November 03, 2020, from
https://www.thedailymba.com/2019/07/26/how-poor-data-quality-negatively-impact-your-business/
Oracle, D. (2008, July 01). Data Mining Concepts. Retrieved November 03, 2020, from
https://docs.oracle.com/cd/B28359_01/datamine.111/b28129/process.htm
Talend, C. (2020, July 17). What is Data Mining? Definition and Examples. Retrieved November 03, 2020, from
https://www.talend.com/resources/what-is-data-mining/
Greiner, L. (2011, January 07). What is Data Analysis and Data Mining? Retrieved November 03, 2020, from
https://www.dbta.com/Editorial/Trends-and-Applications/What-is-Data-Analysis-and-Data-Mining-73503.aspx
Oracle, D. (2008, July 01). Text Mining Concepts. Retrieved November 03, 2020, from https://docs.oracle.com/cd/B28359_01/datamine.111/b28129/text.htm
Flair, D. (2018, September 21). What is Text Mining in Data Mining – Process & Applications. Retrieved November 03, 2020, from
Rouse, M. (2020, September 24). What is text mining (text analytics)? – Definition from WhatIs.com. Retrieved November 03, 2020, from https://searchbusinessanalytics.techtarget.com/definition/text-mining