Big Data: How The Information Revolution Is Tra... -

Traditional statistics rely on small samples to represent a whole. Big data allows us to analyze nearly every data point in a set, which eliminates sampling errors and lets us "zoom in" on small subgroups without losing reliability.

Companies like Netflix and Amazon use "data exhaust"—the trail of digital interactions we leave behind—to forecast hits and provide personalized recommendations. Secondary uses of data, such as using global transaction records to forecast GDP, often hold more value than the data's original purpose.

Google demonstrated big data's power by analyzing search terms for "flu" or "cough medicine" to predict the spread of H1N1 faster than official government statistics. Big Data: How the Information Revolution Is Tra...

Predictive analytics are used to identify early warning signs of infection in premature babies before symptoms appear. Large-scale genomic sequencing is also enabling personalized medicine tailored to an individual’s genetic profile.

In the past, data had to be meticulously cleaned because any error in a small sample was catastrophic. With massive datasets, a sense of general direction is often more valuable than knowing a phenomenon down to the "inch or atom". Traditional statistics rely on small samples to represent

Despite the benefits, Mayer-Schönberger and Cukier warn of a "dark side":

"Smart cities" utilize sensors and traffic cameras to optimize energy use and improve public service delivery in real time. Risks and Ethical Challenges Secondary uses of data, such as using global

The authors identify three core shifts in how we handle information: