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1. What are the business costs or risks of poor data quality? Support your discussion with at least 3 references. Data area utilized in most of the activities of corporations and represent the premise for choices on operational and strategic levels. Poor quality information will, therefore, have considerably negative impacts on the potency of a company, whereas good quality information is typically crucial to a company's success. The development of information technology throughout the last decades has enabled organizations to gather and store huge amounts of data. However, because the data volumes increase, thus will the complexity of managing them. Since larger and additional complicated info resources are being collected and managed in organizations nowadays, this implies that the chance of poor data quality increases.Poor data quality might have significant negative economic and social impacts on an organization.The implications of poor data quality carry negative effects to business users through: less client satisfaction, increase in running prices, inefficient decision-making processes, lower performance and low employee job satisfaction. References: 1. Haug, A., Zachariassen, F., & van Liempd, D. (2011). The cost of poor data quality. Journal of Industrial Engineering and Management, 4(2), 168-193 2. https://www.edq.com/blog/the-consequences-of-poor-data-quality-for-a-business/ 3. Knowledge Engineering and management by the masses. 17th International Conference,EKAW 2010,Lisbon,Portugal,October 11-15,2010 Proceedings 2. Data Mining: Data Mining is an analytic method designed to explore knowledge (usually massive amounts of data - generally business or market connected - conjointly called "big data") in search of consistent patterns and/or systematic relationships between variables, and then validate the findings by applying the detected patterns to new subsets of data. The ultimate goal of data mining is prediction - and predictive data mining is that the most typical sort of data processing and one that has the foremost direct business applications.The process of data mining consists of three stages: (1) the initial exploration, (2) model building or pattern identification with validation/verification and (3) deployment. Reference: 1. Three perspectives of data mining Zhi-Hua Zhou. 2. http://www.statsoft.com/Textbook/Data-Mining-Techniques 3. https://paginas.fe.up.pt/~ec/files_0506/slides/04_AssociationRules.pdf 3. Text Mining: Text mining and text analytics area broad umbrella terms describing a variety of technologies for analyzing and processing semi-structured and unstructured text data. The unifying theme behind every of those technologies is that the ought to “turn text into numbers” thus powerful algorithms will be applied to giant document databases.Converting text into a structured, numerical format and applying analytical algorithms require knowing how to both use and combine techniques for handling text, starting from individual words to documents to entire document databases. References: 1. Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis. 2. http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.0040020 3. https://www.elderresearch.com/hubfs/Whitepaper_The_Seven_Practice_Areas_of_Text_Analytics_Chapter_2_Excerpt.pdf