What’s in a Number? The Role of Big Data in the Music Industry

Success in the music industry has long been about the numbers. For many decades, the music charts were determined by music sales — data which arrived days, weeks or months after the act of making a purchase. However, the digital era has delivered a double whammy for the use of data in the music industry.

Firstly, the rise of streaming means that services such as Spotify can keep a record of each play of a song and attribute it to an individual user. Spotify is only one example of a digital service provider (DSP) which ended 2018 with more than 200 million monthly active users. It’s estimated these users stream around 750,000 songs each minute of each day. These plays can be measured from moment to moment, in real time.

Alongside the availability of data, the computational ability to process mass volumes of data has also advanced. Like the mythical Ouroboros, big data and artificial intelligence have become one machine that feeds itself. The more data there is to consume, the smarter the AI algorithms can become, which in turn generate more data and information to consume. Services such as Spotify use our listening choices to feed their algorithms, which deliver us more of the kind of music we like.

Meta is Better

In data terms, a song is far more than just a title and an artist name. Any given song on a streaming platform comes with its own metadata attached to it. This may include the year of release, the publisher, producer or songwriter — kind of nitty-gritty information that the older generation was accustomed to seeing on the back of an album cover.

In the early days of digital music, DSPs main priority was boosting their own user numbers. This meant they were more concerned with stocking up their music libraries than they were with ensuring that the metadata for every song was meticulously included in their databases. At the time, from the DSPs perspective, what use would all that data have been?

Few in the music industry could have foretold just how big streaming would have become, or that the world would shift to data-driven decision making. However, missing metadata meant that attribution soon became one of the biggest challenges in digital music.

From Rhythms to Algorithms

Fast forward a few decades, and humankind is waking up to the power of what we can do with big data. The music industry has long since shifted to measuring the consumption of music rather than sales with streaming charts having overtaken sales charts as a measure of popularity. Thankfully, the importance of metadata is now far better understood. DSPs now churn out vast quantities of data. Of course, they use this to their own advantage. Each user of a DSP receives customized recommendations based on their personal listening history, which have been selected by algorithms designed to keep the user on the DSPs platform.

But what about the wider music industry? For a sector which was for so long focused on rhythms rather than algorithms, the full potential of big data in music is still largely unrealized. But there are enormous opportunities.

The role of A&R used to involve listening to CDs sent in by aspiring artists, or scouring local venues for hidden gems of talent. Now, AI robots could sweep data from social platforms, DSPs, or music blogs, providing insights to A&R teams that will enable them to scope out new acts anywhere around the globe.

Data about listener demographics, such as age or geography can help marketing and promotional teams make decisions about where to target their efforts. If an artist goes on tour, listener data can inform the decisions about which locations and venues will achieve the most ticket sales. The age of listeners can help to decide which advertising platforms will work best.

Listen to the Radio

One challenge with DSP data is that the sources are fragmented. Spotify, YouTube, and Apple all have their own analytics platforms for those wishing to use the data generated by their users. There have been previous attempts to create a global repertoire of music. One of the most recent came from the European Union, in a project that brought together more than eighty organizations. It’s failure in 2015 left a trail of debt amounting to nearly $14m.

Any attempt to unite disparate groups of humans around the need to harness technology seems bound to fail. At Utopia, we’ve developed a proprietary algorithmic big data engine that can crawl and fetch digital airplay lists. Currently, it’s tracking over 100,000 radio stations, recording over 4.5 million new song usages daily.

In the future, the Utopia BDE will include a wide variety of digital music reporting sources including DSPs, social networks, blogs, community media and many more. It will crunch all this data to explain and predict music industry trends. For the first time, the music industry will find itself on the bleeding edge of data and analytics, rather than fighting to keep up with the challenges that came with the shift to digital music.

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