Summary of Predictive Analytics
The Power to Predict Who Will Click, Buy, Lie, or Die
Credit scores were just the start. Big business and big government soon will anticipate your every move.
Predictive analytics (PA) is a concept that’s both undeniably powerful and potentially creepy. This branch of computer science combines big data with statistics to foretell what you might buy, how you might vote or when you might die. Author and predictive analytics guru Eric Siegel is an unabashed cheerleader for his discipline, and he mostly brushes off the privacy concerns it raises. But that’s not to dismiss his study, which is engagingly written and elegantly translates dense materials and abstract concepts into easy-to-read prose. Siegel draws on a number of real-world examples from well-known companies, including Target, Hewlett-Packard and Chase Bank, and he describes his own experience as an expert consultant on predictive analytics. The result is an enlightening, plainly written guide. getAbstract recommends his PA overview to managers and investors seeking insight into a fast-growing corner of the tech economy.
In this summary, you will learn
- How the popularity of predictive analytics exploded,
- How organizations use predictions and
- How decision trees work.
Comment on this summary
1 week agoGood introduction. It didn't answer the question posted at beginning on how to *handle* privacy concerns.
1 month agoThis summary was a great read! Although I am not heavily involved with Analytics in my current position, analyzing data has always intrigued me. After reading this summary, I realize why - the predictive analytics side of it! I especially like the part about Decision Trees: 'Decision trees are “simple, elegant and precise” and “practically mathless.”' Math was one of my favorite subjects growing up; however, if I can do something more simply and get good results out of it, I will go with that option over using complex data with statistical formulas when and where I can! :)
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