As Data Science is becoming pervasive across so many industries, Hollywood is certainly not being left behind. Learn about how Big Data, analytics, and AI are now core drivers of the movies we watch and how we watch them.
There are countless factors at play in filmmaking, from determining production costs to developing targeted marketing campaigns. Data science is involved in practically every step of the process, and professionals who work in data science can learn many things from the film industry.
Streaming services are at the forefront of the data science revolution. Production companies, including Amazon, Hulu, and Netflix, analyze patterns in Big Data to determine the types of content they create and make personalized viewing recommendations. In this way, data science can aid the art of producing and marketing entertainment at levels never before seen.
The field of data science also pops up as meaty subject matter in a variety of films. The stories of real-life innovators such as Alan Turing and John Nash have been turned into major films in recent years, living alongside fictionalized tales that use predictive analysis, machine learning, and AI as central plot themes.
Society’s fascination with the implications of data science indicates that more films on the subject are sure to come. Further, production companies will continue to use the technology to better understand individual viewing habits and preferences to create content that appeals to the masses.
Technology can inform filmmakers how they should produce and market any given movie. From casting decisions to even the colors used in marketing, every facet of a movie can affect sales. Using technology, we can predict customer preferences and determine how to optimize content to reach its maximum potential.
Predicting what audiences want from a film almost guarantees that film’s success. In 2018, 20th Century Fox, which was acquired by the Walt Disney Company this year, released a paper outlining how it analyzes the content of movie trailers using machine learning. Data collected in the process is used to compare trailers and predict what other films might interest those who watched a particular trailer.
20th Century Fox used Google servers and the open-source AI framework TensorFlow to create Merlin, an “experimental movie attendance prediction and recommendation system.” In Merlin’s trial run, the tool analyzed the trailer of “Logan,” an origin story of the superhero Wolverine, to predict other movies that “Logan” viewers might be interested in. Of the 20 predicted, 11 were correct.
The top five actual movies were all in the predicted list: X-Men: Apocalypse; John Wick: Chapter 2; Doctor Strange; Batman v. Superman: Dawn of Justice; and Suicide Squad. Generally, the audience was looking for a superhero movie that featured a “rugged male action lead.”
While its data interpretation wasn’t perfect, Merlin is a prime example of the evolution of software development over the last decade. For programmers to better concentrate on improving AI algorithms, future software development must include time-saving measures designed to reduce time spent on menial tasks. As AI is designed to focus on a single task, it’s an ideal starting point in improving the accuracy of data analysis within programs.
When big data first hit the scene around 2010, it effectively changed the methods used to turn data analytics into useful insight and profit.
