Predictive Analytics

The company challenged the world by requiring that the winner improve upon Netflix’s own established recommendation capabilities by 10 percent. Netflix is a prime example of PA in action, as a reported 70 percent of Netflix movie choices arise from its online recommendations.

Countless sensors deploy daily. Mobile devices, robots, and shipping containers record movement, interactions, inventory counts, and radiation levels. Personal health monitors watch your vital signs and exercise routine. The mass migration of online applications from your desktop up into the cloud (aka software as a service) makes even more of your

In principle, the math getting us in trouble could also remedy the problem by quantifying prejudice. But that could be done only by introducing the very data element that—so far—remains outside the analysis, albeit inside the eye of every profiling police officer: race.

Their PA projects include customer loss prediction, sales lead scoring, and supplier fraud detection.

research, since we may have limited data but many ideas to try out, we often run the risk of stumbling upon a correlation only by chance—one that’s not ’real’ and won’t continue to hold in the future.”

reinterpreted by listening to it in a new way that uncovers new meaning and insight.

Facebook conducts controlled experiments to see how changes to the rules driving which friends’ posts get displayed influence your engagement and usage of the product.

Overall, data is growing at an incomprehensible speed, an estimated 2.5 quintillion bytes (exabytes) of data per day. A quintillion is a 1 with 18 zeros.

Data more than doubles every three years.

one individual probability by its nature oversimplifies the real-world thing it describes. If I miss a single credit card payment, the probability that I’ll miss another this year may quadruple, based on that factor alone. But if you also take into account that my roof caved in that month, your view will change.