Analytics

There were several options for today:

  1. “U.S. Spies on Millions of Cars,” Wall Street Journal, January 27, 2015 A1. The Department of Justice has a database collecting information from license-plate readers.  What could they do with that information?
  2. “Ex-CIA Officer Convicted of Leaking Secrets,” Wall Street Journal, January 27, 2015 A4. A former CIA agent convicted of leaking secrets to a reporter.
  3. “A Big Step Away From Clutches of Cable TV,” Wall Street Journal, January 27, 2015 B1. Ways to reduce your overall cost of TV.
  4. “Fed Aims to Quicken Payment System,” Wall Street Journal, January 27, 2015 C2.  Expediting the US payments system.  We don’t think of money as information, but money bears many likenesses to information.

But no.  My class at Rice is discussing analytics, among other things, on Wednesday.  So this one caught my eye. “The Signs Before an Affair,” Wall Street Journal, January 27, 2015 D1.  Based on data from 8 million men on a website for people looking for extra-marital affairs, here are factors that might allow one to predict whether his or her spouse will be unfaithful:

  1. Gender – males worse than females (but what was the data they picked from?)
  2. Age – people approaching a milestone birthday, or between 35 and 50
  3. Opportunity – “environmental” risk
  4. History of faithlessness – two sides to this coin
  5. Relationship dissatisfaction – major risk factor
  6. Personality – certain personalities more prone to cheating

If you have a large collection of data, what would it show? What can you use it for?  Who would be interested in your findings?

 

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Filed under Analytics, Collect, Controls, Internal controls, Privacy, Third parties, Use

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