According to a New York Times story, this is exactly what happened to a customer of the large retailer Target. Practically speaking, Target's business analytics activities informed the father that his daughter was pregnant. Specifically, Target statistician Andrew Pole used data-mining techniques to create a "pregnancy predictor" based on online shopping activity. If a customer scored high enough on the pregnancy predictor, Target would send e-mails with offers for pregnancy-related products: a Take a fictional Target shopper named Jenny Ward, who is 23, lives in Atlanta and in March bought cocoa-butter lotion, a purse large enough to double as a diaper bag, zinc and magnesium supplements and a bright blue rug. There's, say, an 87 percent chance that she's pregnant and that her delivery date is sometime in late August 2012. Data privacy debate aside, the Target example is a brief illustration of the insights that can be gained through leveraging big data in an effective business analytics practice. If you are reading this Module, we assume you see the importance, as we do, of using business analytics to positively affect your organization. You may be a business leader who wants to learn more about how companies use data effectively. You may be an analytics manager who wants to understand pitfalls to avoid that can lead to failure.
To Do:
Cite the things you can do to get all the data from scratch.
1. What steps you can do after you have the data on hand?
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Answer:
that's what I was thinking too much about ten minutes ago but still good