W 3 Response 1 (MS)

1) What are the business costs or risks of poof data quality? 

Poor quality data can imply a multitude of negative consequences in a company. To start with, poor quality data that is not identified and corrected can have significant negative economic and social impacts on an organization. The implications of poor-quality data carry negative effects to business users through less customer satisfaction, increased running costs, inefficient decision-making processes, lower performance and lowered employee job satisfaction. Poor data quality also increases operational costs since time and other resources are spent detecting and correcting errors. Poor quality data cannot be trusted and may result in the inability to make intelligent business decisions. Since data are created and used in all daily operations, data are critical inputs to almost all decisions and data implicitly define common terms in an enterprise, data constitute a significant contributor to organizational culture. Thus, poor data quality can have negative effects on the organizational culture. Poor data quality also means that it becomes difficult to build trust in the company data, which may imply a lack of user acceptance of any initiatives based on such data. 

2) What is data mining? 

Data Mining can define as the discovery of knowledge from structured data. Today most available business data is unstructured information; even though it may also contain numbers, dates, and facts in structured fields, unstructured information is typically text. The presence of unstructured information makes it more difficult to effectively perform knowledge management activities using traditional business intelligence tools.

Data mining system can be categorized according to various criteria, as follows:

• Classification according to the kinds of databases mined

Classification according to the kinds of knowledge mined

Classification according to the kinds of techniques utilized

Classification according to the application adapted 

3) What is text mining?

Text mining is the process of exploring and analyzing large amounts of unstructured text data aided by software that can identify concepts, patterns, topics, keywords and other attributes in the data. It's also known as text analytics, although some people draw a distinction between the two terms; in that view, text analytics is an application enabled using text mining techniques to sort through data sets. 

References:

Haug, A., Zachariassen, F., & Liempd, D. V. (2011). The costs of poor data quality. Journal of Industrial Engineering and Management, 4(2). doi:10.3926/jiem.2011.v4n2.p168-193

Loshin, D. (2011). Business Impacts of Poor Data Quality. The Practitioners Guide to Data Quality Improvement, 1-16. doi:10.1016/b978-0-12-373717-5.00001-4

Timmer, A. (2015). Poor Data Quality. Deutsches Aerzteblatt Online. doi:10.3238/arztebl.2015.0544a

Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques. Amsterdam: Elsevier/Morgan Kaufmann.

Tan, P., Steinbach, M., Karpatne, A., & Kumar, V. (2019). Introduction to data mining. New York, NY: Pearson Education.

Ignatow, G., & Mihalcea, R. (2018). An introduction to text mining: Research design, data collection, and analysis. Thousand Oaks, CA: SAGE Publications.

Zanasi, A., Brebbia, C. A., & Ebecken, N. F. (2005). Data mining VI: Data mining, text mining, and business applications. Southampton, UK: WIT.

Jo, T. (2019). Text mining: Concepts, implementation, and big data challenge. Cham, Switzerland: Springer.