need for data mining

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Topic: The Need for Data Mining

Overview: Over the last decade, advances in processing power and speed have enabled us to move beyond manual, tedious, and time-consuming data mining practices to quick, easy, and automated collection for data analysis. The more complex the data sets, the more potential there is to uncover relevant insights. Retailers, banks, manufacturers, telecommunications providers, and insurers are using data mining to discover relationships among everything from price optimization, promotions, and demographics to how the economy, risk, competition, and social media are affecting their business models, revenues, operations, and customer relationships.

Select one of the following industries and discuss the required topics:

  • Medical
  • Banking/Finance
  • Marketing/Sales
  • Science and Engineering
  • Insurance
  • Retail

Guidelines for Submission: Using APA 6th edition style standards, submit a Word document that is 2-4 pages in length (excluding title page, references, and appendices) and include at least two credible scholarly references to support your findings.

Include the following critical elements in your essay:

I. Use Case: Present an industry use case discuss the need for data mining in this particular industry. Describe how data mining can help organizations within the selected industry to retrieve the valuable information from the huge amount of data they collect. Explain how they make the data usable for analytical purposes, for business use, or for strategic planning purposes as a result of data mining processes.

I. Data Mining Challenges: Describe one challenge associated with data mining practices and processes within this industry. How can organizations overcome this challenge to gain a competitive advantage over their competitors?

III. Data Mining Techniques: Describe one data mining technique that would be useful for collecting the data required to support the industry and foster effective data analysis processes for decision-making (clustering, classification, pattern matching, association, regression, visualization, or meta rule-guided mining, for example). How will this technique help organizations in this industry overcome the challenge you mentioned?

 
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