The New York Times posted this useful article yesterday about the power and peril of predictive analytics.  The author is not quite right when he states "If Netflix can predict which movies I like, surely they can use the same analytics to create better TV shows. But it doesn’t work that way. Being good at prediction often does not mean being better at creation." 

Any good predictive algorithm can find the features of movies I like as part of a recommendation engine.  And yes it cannot create the next movie I will like but it surely can inform writers about making sure the features I like are in the next movie they create. 

And the author is certainly not right in his contention "For example, we may find the number of employees formatting their résumés is a good predictor of a company’s bankruptcy. But stopping this behavior hardly seems like a fruitful strategy for fending off creditors."  Here he assumes understanding the outcome of a system is the same as changing it.  Not anymore so than when your doctor tells you to take aspirin for the headache you have due to a virus.  The aspiring relieves a symptom but not the disease. 

But the author is dead on with this statement "Rarity and novelty often contribute to interestingness — or at the least to drawing attention. But once an algorithm finds those things that draw attention and starts exploiting them, their value erodes. When few people do something, it catches the eye; when everyone does it, it is ho-hum." 

In short, if you are the first to discover signal from what looked like noise then use it to your advantage.  But recognize that once everyone else notices how well you are doing by manipulating the system they will copy you.  The entire system will change and your advantage will disappear. 

But all is not lost.  You simply have to build your model again and find a new first mover advantage.  Don't rest on your laurels. 

 

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