Five ways automated data analytics keeps the beat going

Sound generators, virtual instruments, streaming; there have been so many amazing technological advancements in the music industry in recent years.

The 90s saw a big uptick in technology, but the industry wasn’t always able to keep up, causing many consumers to turn to illegal downloads and peer-to-peer platforms for convenience.

But today, the evolution of big data and automation has changed the landscape: streaming services have made illegal downloads a thing of the past. A closer relationship has been formed between avid fans and music labels. And music providers can now unlock valuable insights into their audience’s listening habits.

Let’s take a closer look at some of the latest applications for data automation in the music industry.

1. AI-powered A&R discovery

Music publishing companies and record labels often have their own A&R (artist & Repetoire) discovery division which is responsible for finding the ‘next big thing’. But scouting for emerging talent manually can be time-consuming, as it involves sifting through hundreds of different artists and music data (social, broadcast, streaming, touring).

By using predictive analytics and AI, A&R teams can accelerate the talent-scouting process, in turn saving on costs and resources. Data-driven scouting tools use machine learning to collect data from a variety of sites such as Spotify, social media, or music-specific blogs. This data can then be used to easily identify new talent, as well as filter out those that aren’t a good match for the label.

2. Data-driven royalty collection

In order to allocate royalties to artists, music consumption has to be tracked. With such a large ecosystem at play (music is spread across multiple streaming sites, blogs, and social media), tracking all this metadata can be a challenge. This has directly impacted artist compensation, with hundreds of millions in royalties left unaccounted for.

Advanced data analytics, however, plays a key role in the effectiveness of royalty collection tools today. Verified credit platforms combine data analytics and AI, enabling artists and copyright holders to measure how many consumers listen to or download their music each day, as well as identify missing legacy credits.

3. Automated music mastering

For many years, music mastering (adjusting elements of a stereo mix to optimize playback) has been done manually by sound engineers. Depending on the size of the project, this process can take hours, leading to mounting costs.

This is where automated music mastering tools come into play. Since they rely solely on algorithms, machine listening and reference tracks to generate or master music, less manual input is required when it comes to optimizing a track.

4. Predicting the next craze

Finding the next big ‘hit’ – especially amongst listeners – has always been an objective for record labels and music publishing companies. What will make people tick? What song will be the most popular? What will drive the most downloads/shares/likes?

In 2015, researchers at the University of Antwerp were one of the first to create a tool that predicted possible musical hits, which proved reasonably successful. Leading publisher, VICE, even conducted a test on the tool’s accuracy, running the year’s top Billboard dance hits through the algorithm. All of the songs (which became a hit) were predicted to have a 65% of doing so.

5. Music streaming

Music streaming providers such as Spotify use automated data analytics to harness their ever-growing data volumes and data sources. This data can then be used to unlock valuable insights into tracks, artists, and listener sentiment.

Rather than engineering teams having to spend months building a custom data infrastructure, music streaming companies can plug into an enterprise data automation solution to consolidate hundreds of data streams from across dozens of partners in a matter of weeks – accurately, transparently, and on a global scale.

Through automation, these streaming providers can:

• pull the freshest data in real time and maintain consistently high-quality datasets to present to other stakeholders
• model data using their own business rules and embed them into a single, change-controlled, source of truth
• fully integrate with existing engineering systems including data warehouses
• quickly recover following any partner outage or schema change, by rapidly identifying glitches and automatically backfilling with accurate data
• add or change partners with no disruption to the business

The examples above show how far we’ve come in automating data analytics already. By leveraging advanced music technology, combined with powerful analytics software, we can help brands unlock valuable insights, build stronger relationships with listeners, and help make the industry a fairer and more efficient place for record labels, music publishers, and consumers alike.