Big Data Analytics, House of Cards and Future of Television Creation & Consumption
There are some posts, posted earlier on this blog, which talk briefly on how Netflix used Big Data Analytics to come up with the theme, plot, characters, actors and other elements of their original series, House of Cards. (Post link1, link2). This post dives deeper into how Big Data Analytics and Algorithmic Programming will shape the future of T.V. Series. This partially ties with the discussion in class in Session 6 regarding how consumer behavior is studied, modeled and influenced.
The television consumption industry is changing at a very brisk pace. People now expect television to fit into their own schedules and it is predicted that weekly scheduled television broadcasts will soon be eclipsed by on-demand entertainment. (Leber, 2013).
There has been a lot of buzz and stir in Data Analytics and Television Media circles since the creation of House of Cards. Netflix knew that its original series would be a grand hit based on the data of viewing habits of its 33 million users (Leber, 2013). Based on these tagging and recommendation system Netflix could understand
- how viewers were enticed by political dramas (like British version of House of Cards)
- how large proportion of viewers liked watching actor Kevin Spacey in the direction of David Fincher
- how different trailers attracted the attention of different users.
and use this data for the creation of the series, which was made available all at once. Netflix was able to leverage its long lasting relationship with customers and using that data of their viewing habits they were able to place their bets and produce ‘House of Cards’.
How much and what kind of data does Netflix deal with?
To put the movie viewing habits of viewers in perspective, the number of people watching movies streamed online was higher than those watching them on physical DVDs in 2013-14 (Sandvine, 2014). Also a third of data buffered during peak hours was due to movies streamed online.(Sandvine, 2014). Netflix mines the data of around 30 million ‘plays’ a day, including when their users pause, rewind and fast forward, 3 million searches, 4 million ratings by Netflix subscribers as well as the devices on and time of day when shows are watched.(GigaOm, 2012). In addition, TV shows and movies on Netflix are annotated with hundreds of tags inserted by viewers to describe the genre, the tone, the action and the talent, among plethora of other things. While in the past, those tags were used to recommend other shows by Netflix from a long list of titles on the service, now Netflix is using them in the creation of original content because it knows what people want. (Carr, 2013). While looking back at the show created and application of data analytics, begs to ask a more fundamental question, ‘Can creativity be automated’?.
Steiner, in 2012, in his article titled the same, started with the premise that creativity can’t be copied by machines as creativity is believed to be the product of mysterious processes within the right side of the human brain but later tells that complex algorithms are moving into creative fields and proving that in some of these pursuits and humans can be displaced. This is already observed in the case of music composers and creative writing. He also mentions that David Cope, a professor emeritus at UC Santa Cruz, believes that technology is almost there in not only replicating and reusing good music but creating new music. (Steiner, 2012)
Cope has been weaving thousands of lines of LISP code into music-making algorithms for 30 years. At first this produced crude music unfit for public performance, but now his algorithms compose music that imitates masters like Johann Sebastian Bach so well that people can’t always tell the difference. Cope feeds music to machine-learning algorithms that create new compositions by changing and building on patterns it finds in existing music. But his latest algorithm, which he’s dubbed Annie, takes programmed creativity yet a step further. She decides on the musical patterns, the criteria, and ultimately, the path she takes to making music. “I have no idea what she’s going to do sometimes,” Cope says. “She surprises me as much as anybody.”
Implications on Television Content Creation and Consumption
As companies like Google, Netflix, and Amazon, which know more details about our detailed watching habits, start to become more dominant forces in the creation of original programming, they could start also shaping creative decisions of directors and writers as well (Leber, 2013). In the years to follow all aspects of television content making including screenplay may be written to meet the whims of data-driven media streaming companies. Also, consumption, as mentioned earlier in the article, is believed to entirely move from broadcast schedules to on – demand viewing.
IBM defines ‘Big data analytics’ as
the use of advanced analytic techniques against very large, diverse data sets that include different types such as structured/unstructured and streaming/batch, and different sizes from terabytes to zettabytes. It has typically has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media – much of it generated in real time and in a very large scale. Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable.
- Leber, J., 2013. “House of Cards” and Our Future of Algorithmic Programming [Online] Available at: http://www.technologyreview.com/view/511771/house-of-cards-and-our-future-of-algorithmic-programming/ [Accessed 17 10 2014].
- Sandvine, 2014. Global Internet Phenomena. Available at: https://www.sandvine.com/downloads/general/global-internet-phenomena/2014/1h-2014-global-internet-phenomena-report.pdf/ [Accessed 17 10 2014].
- GigaOm, 2012. Netflix analyses a lot of data about your viewing habits. Available at: http://gigaom.com/2012/06/14/netflix-analyzes-a-lot-of-data-about-your-viewing-habits/ [Accessed 17 10 2014].
- Carr, D., 2013. Giving Viewers What They Want. Available at: http://www.nytimes.com/2013/02/25/business/media/for-house-of-cards-using-big-data-to-guarantee-its-popularity.html [Accessed 17 10 2014].
- Steiner, C., 2012. Can Creativity Be Automated? MIT Technology Review June 2012.