Big data drives today’s movie industry, both in terms of the amount of data packed into each frame you see at theaters and in terms of video streaming online. It’s what delivers 3-D effects in the theater and personalized recommendations to Netflix viewers. And very big numbers ride on both.
In the past few years, 3-D movies have staged a comeback on a scale much greater than their brief heyday in the 1950s. Adding in the 3-D effect adds "anywhere from 100% to 200% more data per frame," according to Jeff Denworth, vice president of marketing for DataDirect Networks (DDN). Denworth attributes the proliferation of present-day 3-D films to the huge success James Cameron had with the 3-D film "Avatar" in 2009, which packed a petabyte of data.
"Avatar" cost about $237 million to produce, but it brought in more than ten times that amount. It earned the distinction of IMDB identifies it as "the highest-grossing film of all time." By the beginning of 2010, it had taken in $2,779,404,183. A rash of 3-D films followed this success, and many did very well. According to iSuppli Market Intelligence (owned by IHS) in 2011 3-D films brought in $7 billion at the box-office, 16 percent more than the previous year.
The full figures for 2012 are not yet in, though they will likely be higher as the number of 3-D screens have gone up from about 9,000 in "2009 to 43,000 in by the third quarter" of 2012. One of the biggest draws of the year, Marvel’s 3D superhero flick, "The Avengers," grossed $1,511,757,910 in 2012. As 3-D has grown so common at the theater, movie-makers have to point to something else to distinguish their offering.
"The Hobbit: An Unexpected Journey" had to do 3-D one better with its "brand new format High Frame Rate 3D (HFR 3D)." Instead of the 24 frames per second, which is the movie standard, it packs in 48. The advantage to the viewer, it claims, is that the greater number offers an experience "closer to what the human eye actually sees." Perhaps so, but quite a number of viewers were less than thrilled by the effect. Nevertheless, by December 29, 2012, "The Hobbit" had already taken in $600,508,000, according to IMDB figures.
Big data is also the key to watching movies on the small screen. Instead of picking up a disc when they buy or rent a movie, people now can just have it come right to them. As Dan Cryan, senior principal analyst at HIS observed, in 2012 Americans made "a historic switch to Internet-based consumption, setting the stage for a worldwide migration from physical to online."
Estimates of online movie payments for the US in 2012 are “3.4 billion views or transactions, up from 1.4 billion in 2011. This form of video streaming is dominated by Netflix in the US, where it makes up "33% of peak period downstream traffic" Amazon, Hulu, and HBO Go follow far behind at 1.8%, 1.5%, and .5% respectively. It intends to keep its lead with the help of big data.
Netflix was the subject of a WSJ blog on using big data to improve streaming video. Though Netflix still offers to mail out the DVDs people select for rental, more customers now opt for streaming. In the interest of improving efficiency on that end, Netflix transferred its holdings to Amazon’s cloud. It also started using Hadoop, which enables it "to run massive data analyses, such as graphing traffic patterns for every type of device across multiple markets." That helps plan for improved data transmission and better understanding of the customer.
In addition to using big data solutions for delivery of content, Netflix applies algorithms to predict what their customers would likely want to watch next. This type of data mining technology makes Netflix confident that it can handle hosting original content. In fact, it bet more than $100 million on it; that’s the reported sum paid for the rights to two seasons of House of Cards, one of several original content series it plans on streaming.
As Netflix’s Chief Communications Officer, Jonathan Friedland, says, "We know what people watch on Netflix and we’re able with a high degree of confidence to understand how big a likely audience is for a given show based on people’s viewing habits."
So what do you think? Is it possible to guarantee a hit with big data?
