Eric Siegel answers eight questions about predictive analytics

Eric Siegel
Eric Siegel
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Published 2013-02-15
Eric Siegel, author of "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die" (Wiley, 2013 - www.thepredictionbook.com/), answers these eight questions:

1. What is predictive analytics?
2. Why is predictive analytics important?
3: Isn't prediction impossible?
4. Is predictive analytics a big data thing?
5. Did Nate Silver use predictive analytics to forecast Obama's elections?
6. Does predictive analytics invade privacy?
7. What are the hottest trends in predictive analytics?
8. What is the coolest thing predictive analytics has done?

About the book:

This rich, entertaining primer by former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of predictive analytics, showing how predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime-fighting, and boosts sales.
All Comments (7)
  • @jagusiff
    If you aren't making Predictions and determining when you are right or wrong you have no means of Learning; and, therefore, be able to make better Business Decisions in the future.
  • @mattl5055
    Another problem with Predictive Analytics when applied to business is meeting the statistical assumptions. How could you meet the normality assumption with business data? Most business data is very skewed. You could use non-parametric methods but that is weak. 
  • @simeon24
    *cough* prism, riot, perfect citizen, private interest *cough*
  • @mattl5055
    Predictive Analytics and 'Big Data' is simply a business buzzword of this decade, you will probably never hear about this again in a couple of years because companies will find out that it does not work. It does not work in business because you cannot obtain data for all of the independent factors that has a casual relationship with the dependent factor, for example if your dependent is sales, how are you going to obtain data for factors such as brand equity, word of mouth sales, creative etc that might have a casual relationship with sales? What will happen when you do predictive analysis is an overcompensation for the independent factors that you have included in your model (see Omitted Variable Bias). Also, with business data, most of the independent factors you believe will have a casual relationship with the dependent factor will likely to show no relationship when you have conducted a sig test. If say the relationship between the factors are strong, how can you be sure that there is no confounding factor involved? Another fault with predictive is the bias modelling software they use (like eviews) which transforms data in a way that it fits into the model. It is like fitting a square into a circle. Because these software do not look at whether these factors should even be in a model, it is up to the user to determine what goes into the model, thereby there would be a possibility of bias. Business people that believes this should read up on 'Cause and Effect', Confounding factors, Omitted Variable Bias, Statistical Errors, Multicollinearity etc