Questions And Case Study Of Analytical

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  1. Selecting the wrong problem for data mining. Not every business problem can be solved with data mining (i.e., the magic bullet syndrome). When there are no represen- tative data (large and feature rich), there cannot be a practicable data mining project.
  2. Ignoring what your sponsor thinks data mining is and what it really can and cannot do. Expectation management is the key for successful data mining projects.
  3. Beginning without the end in mind. Although data mining is a process of knowledge discovery, one should have a goal/objective (a stated business problem) in mind to succeed. Because, as the saying goes, “If you don’t know where you are going, you will never get there.”
  4. Defining the project around a foundation that your data cannot support. Data mining is all about data; that is, the biggest constraint that you have in a data mining project is the richness of the data. Knowing what the limitations of data are helps you craft feasible projects that deliver results and meet expectations.
  5. Leaving insufficient time for data preparation. It takes more effort than is generally understood. The common knowledge suggests that up to one-third of the total proj- ect time is spent on data acquisition, understanding, and preparation tasks. To suc- ceed, avoid proceeding into modeling until after your data are properly processed (aggregated, cleaned, and transformed).

1. What do you think about data mining and its implication for privacy? What is the threshold between discovery of knowledge and infringe- ment of privacy?

2. Did Target go too far? Did it do anything ille- gal? What do you think Target should have done? What do you think Target should do next (quit these types of practices)?

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