In a previous post on this blog, I reviewed what movie ratings teach us about data quality. In this post I ponder another movie-related metaphor for information development by looking at what movie descriptions teach us about metadata.
Nightmare on Movie Night
It’s movie night. What are you in the mood for? Action Adventure? Romantic Comedy? Science Fiction? Even after you settle on a genre, picking a movie within it can feel like a scene from a Suspense Thriller. Even if you are in the mood for a Horror film, you don’t want to turn movie night into a nightmare by watching a horrible movie. You need reliable information about movies to help you make your decision. You need better movie metadata.
Tag the Movie: A Netflix Original
In his article about How Netflix Reverse Engineered Hollywood, Alexis Madrigal explained how Netflix uses large teams of people specially trained to watch movies and tag them with all kinds of metadata.
Madrigal described the process as “so sophisticated and precise that taggers receive a 36-page training document that teaches them how to rate movies on their sexually suggestive content, goriness, romance levels, and even narrative elements like plot conclusiveness. They capture dozens of different movie attributes. They even rate the moral status of characters. When these tags are combined with millions of users viewing habits, they become Netflix’s competitive advantage. The company’s main goal as a business is to gain and retain subscribers. And the genres that it displays to people are a key part of that strategy.”
The Vocabulary and Grammar of Movies
As Madrigal investigated how Netflix describes movies, he discovered a well-defined vocabulary. Standardized adjectives were consistently used (e.g., Romantic, Critically Acclaimed, Dark, Suspenseful). Phrases beginning with “Based on” revealed where the idea for a movie came from (e.g., Real Life, Children’s Book, Classic Literature). Phrases beginning with “Set in” oriented where or when a movie was set (e.g., Europe, Victorian Era, Middle East, Biblical Times). Phrases beginning with “From the” dated the decade the movie was made in. Phrases beginning with “For” recommended appropriate age ranges for children’s movies. And phrases beginning with “About” provided insight about the thematic elements of a movie (e.g., Food, Friendship, Marriage, Parenthood).
Madrigal also discovered the rules of grammar Netflix uses to piece together the components of its movie vocabulary to generate more comprehensive genres in a consistent format, such as Romantic Dramas Based on Classic Literature Set in Europe From the 1940s About Marriage (one example of which is Pride and Prejudice).
What Movie Descriptions teach us about Metadata
The movie metadata created by Netflix is conceptually similar to the music metadata created by Pandora with its Music Genome Project. The detailed knowledge of movies that Netflix has encoded as supporting metadata makes movie night magic for their subscribers.
Todd Yellin of Netflix, who guided their movie metadata production, described the goal of personalized movie recommendations as “putting the right title in front of the right person at the right time.” That is the same goal lauded in the data management industry for decades: delivering the right data to the right person at the right time.
To deliver the right data you need to know as much as possible about the data you have. More important, you need to encode that knowledge as supporting metadata.
