With the surge of digital streaming technologies, the music industry has been forced to look elsewhere for serious revenue. Music is the one universal media that connects the world, and big data is the key to unlock its potential. Big Data and analytics have played a big part in this reinvigorated romance. From recommendation engines designed to choose the perfect individual playlist, to Internet of Things enabled pop concerts, data is redefining the dynamics of the music industry, as well as the relationship between listeners and music, in ever more creative ways. The industry’s aim now is to combine this deeper understanding of its customers with the deeper understanding of the music itself, which Big Data has also made possible. There are two major ways big data is already influencing the music industry: music creation and music selection. The second one, however, is making far more waves. Raw music is essentially unstructured data but because it is easily digitized, it can be quantified and analyzed.
I stream, you stream, we all stream some Trap Queen. Streaming is fast becoming the primary way we consume music, whether that be through the more interactive on-demand services, algorithmically-driven lean-back experiences, the increasingly popular format of human curation and playlists (think Beats One radio or Spotify’s discovery feature), or some combination of the above. Spotify is a commercial digital music streaming service that gives you access to millions of songs. It was launched in 2008 and since then is has registered over 24 million active users of which 6 million are paying users. The have 3.7 million Facebook fans. It has over 20 million songs online and every day 20.000 new songs are added to the database. Users created over 1 billion playlists and over $ 500 million has been paid out to rights holders since the launch of Spotify. Spotify users create some 600 GB of this data daily. But it doesn’t stop there. It is clear that Spotify’s entire business is based on big data and would not be able to exist without it. Spotify knows what listeners like and want. The ability to listen to music is only the most basic feature of Spotify. They constant compile data and create algorithms to suggest new music to listeners. Their Discover Weekly is like a fresh mix-tape made just for you. Powered entirely by algorithms and computers, the mixes are astonishingly well put together.
Users love apps like Spotify, but companies love Shazam, Next Big Sound, Find, and the appropriately titled HitPredictor. Last year, HitPredicor accurately predicted 48 of the top 50 hits. Thanks to their algorithms, there is no longer a need for talent scouts to go crawling through bars, or even overly rely on their “gut.”
This could lead to some very unexpected discoveries. Artists who would otherwise never make it out of their state are much more visible to big, global companies. However, this also creates a large degree of concern among musicians. Allowing listeners to pick the next artists actually means creating a lot of the same music. It seems data-driven music has created an incredible paradox. Any song from any singer now has the capacity to get discovered; yet we are fueling an unusually homogenous series of artists and albums. Only time will tell what the data-driven radio will bring.
In the music industry predicting the future is all-important, and this operates on all scales – from deciding what an individual user of a streaming service wants next in their playlist, to discovering the next Gangnam Style. And recently it has been shown that Big Data has the ability to do just that. Data-driven creation, however, has not changed excessively. Yes, music companies can use data to infer what style will be the most profitable to fund, but data is not creating music from scratch. Yet.
