Two-and-a-half quintillion bytes of data are created daily. This could potentially rise even more thanks to better internet access. Today, we can collect big data from every imaginable field we're able to monitor digitally. Big data refers to large sets of info that are analyzed for trends and patterns that offer useful insights. Special emphasis is placed on analyzing people's behavior and interactions online.

That one-quarter of Medicare spending in the United States occurs in the last year of life is commonly interpreted as waste. But this interpretation presumes knowledge of who will die and when. Here we analyze how spending is distributed by predicted mortality, based on a machine-learning model of annual mortality risk built using Medicare claims. Less than 5% of spending is accounted for by individuals with predicted mortality above 50%. The simple fact that we spend more on the sick--both on those who recover and those who die--accounts for 30 to 50% of the concentration of spending on the dead. Our results suggest that spending on the ex post dead does not necessarily mean that we spend on the ex ante "hopeless."

Big data refers to large sets of info that are analyzed for trends and patterns that offer useful insights. Special emphasis is placed on analyzing people's behavior and interactions online. The challenge, however, is in figuring out the best way to process, analyze and make useful insights of the information gathered. Here is why big data is such a big deal. Data on the kind of entertainment we like (e.g., music, videos, movies) can be analyzed, and the insight drawn from it can be used to tailor make a unique experience just for us.