If we've talked, you probably noticed that I'm a big fan of cross-pollination—looking for lessons in advances in other fields (such as the hospitals who learned process improvement from racing teams). Super Crunchers tells how analytics are helping companies make more effective decisions, and in the process, changing the role of expert opinion.
The examples are all over the place: e-commerce recommendation systems, individualized offers and price discrimination, evidence-based medicine, social programs, dog racing. Example after example of how analytics create new opportunities and challenge the experts in the predicting game.
The nice thing about the book is that you don't need to understand any of the underlying technology going in. Ayres writes for a non-technical audience, and if it's entirely new to you, he won't leave you in the dust. Still, he's a law prof, so if you want documentation, you'll find plenty in the notes. Either way, I recommend reading it to the end, as he eventually switches from examples to explanation—and yes, he explores the risks to privacy and individual freedom, too.
Teaching the machine to read
Analyzing media content—online or otherwise—requires extracting meaning from unstructured text, and that's not in Super Crunchers. Text analytics are just a bit bleeding edge. For now, we can still argue about whether automated analysis is good enough to use. But the success of the algorithms against the experts in purchasing, sports, medicine and law suggests where text analysis is going.
As for the cross-pollination angle, a book this full of examples should be able to inspire some ideas in the reader, whether for your business or your career. For me, the big takeaway was the confirmation of my new favorite question.
