Business Finance
Management Information Systems and Telecommunications Businesses Discussion

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Management Information Systems: Discussion Post- $4- 03/25/2020 1. What are the business costs or risks of poof 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. Reply to post - Vamsi Garimella - $2- 03/25/2020 Different organizations have diverse methods, and ways of checking at an information value and worth, the quality of the data products can significantly impact the business in many ways i.e. Financial implications; the decisions that result from sparse data are non-reliable, ineffective, and costly, “average thee cost financial impact resulting from poor data quality is $9.7 million per year" (Ehrlinger, & Wöß, 2018,). To maintain and put up with lousy data quality is costly and will mean money incurred will rise per activity or project to be accomplished. Quality cost to production; bad data can impact so adversely on employees’ performance, and they may suffer significant setbacks. To manufacture, to do with bad data, it will be time-consuming as a lot of corrections and errors need clarity. Therefore, it will result in more work shifting to manual techniques. The scientist estimates that 50 percent to 80 percent of the time spent in the collection and dissemination of unruly digital data (Krasner, H. 2018). To redo the activities and correct all the problems means that the production slowed, and it is also capital intensive. Data quality on the business reputation; poor data quality can tarnish the business reputation and image as far as the client base is concerned, (Baker, M. 2015). When customer satisfaction goes down, they will give negative information or feedback concerning products or services offered. Question 2 Data mining is a procedure or method of looking for big data stores to come up with conclusive evidence of criteria for doing some simple analytical processes (Aggarwal, 2015). It is thus a process that will require some form of built models in telling this trend. Data mining has been employed by different businesses to convert raw data into some essential and sensible information. They could then be used to generate some form of programs as search tools, websites, and platforms (Han, Pei, & Kamber, 2011). Question 3 Also called text analytics is human-made intelligence, which makes the use of natural language procedure to convert the word formats and other databases into some suitable form to be analyzed by machines and other gadgets,. It is mostly practiced in knowledge-driven companies' businesses. It involves checking at some junk documents to come up with new ideas that can be of help to solving a particular problem (Miner, Elder, Fast, Hill, Nisbet, & Delen, 2012). With the discovery of text mining, organizations have been able to analyze vast amounts of information in the most convenient ways, thereby saving on time and allowing employees to give their best and focus on priorities, References: Ehrlinger & Wöß (2018). Automated schema quality measurement in large-scale information systems. In International Workshop on Data Quality and Trust in Big Data (pp. 16-31). Springer, Cham. Baker, M. (2015). Digital transformation. Buckingham Business Monographs. Krasner, H. (2018). The cost of poor quality software in us: A 2018 report. Consortium for IT Software Quality, Tech. Rep. Han, Pei, & Kamber (2011). Data mining: concepts and techniques. Elsevier. Aggarwal (2015). Data mining: the textbook. Springer. Miner, Elder, Fast, Hill, Nisbet, & Delen, (2012). Practical text mining and statistical analysis for nonstructured text data applications. Academic Press. Reply to Yiming Cai-- $2- 03/25/2020 1. What are the business costs or risks of poof data quality? Support your discussion with at least 3 references. Business costs including hidden costs and obviously costs such as location costs, mortgage, building lease, utilities, phone, computer expenses, equipment, furniture, machinery, vehicles and driving expenses, other services and maintenance expenses, business insurances, wages, salaries, payroll taxes, benefits, suppliers and other office expenses, advertising and marketing costs, travel expenses, tax expenses, etc. The development of information technology during the last decades has enabled organizations to collect and store enormous amount of data. However, as the data volumes increase, so does the complexity of managing them. Since larger and more complex information resources are being collected and managed in organizations today, this means that the risk of poor data quality increases (Watt & Shankaranarayanan, 2009). The risk of poor data quality can have a lot of negative consequences in a company. Poor quality data that is not identified and corrected can have significantly negative economic and social impacts on an organization (Ballou et al., 2004; Wang & Strong, 1996). 2. What is data mining? Support your discussion with at least 3 references. Data mining is the way toward finding designs in huge data including techniques at the crossing point of machine learning, insights, and database frameworks. Data mining is a catch-all term for collecting, extracting, warehousing, and analyzing data for specific insights or actionable intelligence (Garrett Alley, 2019). Data mining and predictive analytics give law enforcement and intelligence professionals the ability to put more evidence-based input into operational decisions and the deployment of scarce resources, thereby limiting the potential waste of resources in a way not available previously (Colleen McCue, 2007). Data mining is used to simplify and summarize the data in a manner that we can understand, and then allow us to infer things about specific cases based on the patterns we have observed (Alexander Furnas, 2012). 3. What is text mining? Support your discussion with at least 3 references. Text mining, also known as text analysis, is the process of transforming unstructured text data into meaningful and actionable information. Text analytics (also known as text mining) refers to a discipline of computer science that combines machine learning and natural language processing (NLP) to draw meaning from unstructured text documents (Tim Mohler, 2019). Text mining utilizes different AI technologies to automatically process data and generate valuable insights, enabling companies to make data-driven decisions. Text mining incorporates and integrates the tools of information retrieval, data mining, machine learning, statistics, and computational linguistics, and hence, it is nothings short of a multidisciplinary field (Abhinav Rai, 2019). By mining this data, they can save operational costs, assist with predicting the future, and uncover insights previously not available (Chris St. Jeor, 2019). References: Data Quality Assessment in Context: A Cognitive Perspective. Stephanie Watts, Ganesan Shankaranarayanan, 2009). Retrieved from Beyond Accuracy: What Data Quality Means to Data Consumers. Richard Y. Wang and Diane M. Strong. 1996. Retrieved from Data Mining and Predictive Analysis. Colleen McCue, 2007. Retrieved from Everything You Wanted to Know About Data Mining but Were Afraid to Ask. Alexander Furnas, 2012. Retrieved from What is Data Mining? Garrett Alley, 2019. Retrieved from What is Text Mining: Techniques and Applications. Abhinav Rai, 2019. Retrieved from Text Analytics: 5 Examples To Open Your Eyes To Your Own Opportunities. Chris St. Jeor, 2019. Retrieved from The 7 Basic Functions of Text Analytics & Text Mining. Tim Mohler, 2019. Retrieved from ...
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Hey buddy, here is the discussion Management Information Systems: Discussion Post- 1.What are the business costs or risks of poof 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.

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Question 1: Poor data quality
In today’s world, businesses could be greatly affected by poor data quality. The more
enrich data quality is, the more convinced the users of the data will be in their productivity. At
the same time, this will ensure lower risks leading to better business decisions and an increase in
efficiency hence saving money (Haug et al., 2011).
Poor Data Quality can result in reputational damage for the organizations. This can vary
though little damage to huge community relations disasters. For example, poor data in the
banking sector could lead to a dilemma if they don’t know the exact information about their
customer, whether he is a civil servant or suspected terrorist financiers, resulting in punitive fines
for the organizations (Loshin, 2011). There could be a chance of falling revenues in many ways
due to poor data. For instance, customer data which is incorrect, would affect the sales and
revenues of the organization to be lost. Once these errors are made to the organization, they can
then be complicated to modify. Many organizations may fail a vital chance for innovative
product development or consumer requirement (Haque ...

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