November 19, 2020

Missing, bad, or inconsistent song metadata is a crisis that has left, by some estimations, billions on the table that never gets paid to the artists who earned that money. And as the amount of music created and consumed continues to increase at a faster pace, it’s only going to get messier.

Volume places a strain on legacy systems

The pressures increasing data volumes place on organisations’ systems can be difficult to cope with. Legacy systems cannot manage the rapidly dynamic scale and speed at which music royalty data must be processed. The music rights industry must find ways to manage these changes or face increasing problems in the future. They need to adapt their processes and technologies to meet the demand of modern music matching. The Matching Engine application works with existing systems to support music matching at scale. The application is built on modern cloud technologies, which helps CMOs to overcome many of the challenges posed by the increased volume of data.

Along with increasing volumes of music streaming data, CMOs must also tackle the issue of bad metadata and data complexities. The media and interest groups have extensively reported the issue of unmatched music royalties, which is threatening the livelihoods of artists and producers. In their 2019 collections report, CISAC President Jean-Michel Marre shared that despite the rise of music streaming, just 1 in 5 collections is from a digital source (CISAC, 2019). There is a huge discrepancy between the music being played and the royalties being paid. Much of this can be attributed to bad, unorganised and inconsistent music metadata.

Increasing volumes mean increased costs

Cost is a priority. For CMOs, increased streaming data would have meant increasing resources and overheads such as costly servers, putting a strain on budgets. However The Matching Engine also adapts costs to manage volume changes, CMOs pay for what they use each month. Also taking advantage of the elasticity of the cloud, CMOs can reduce overheads by reassigning team resources to other roles. Through automation, reporting and cloud technologies, there is no longer a need for constant human monitoring.

The Matching Engine uses automated parameters to identify and match data. A foundation of Azure and Databricks technology provides the ability to process and transform data from various streaming platforms and in different formats at scale.