In Gartner’s glossary, big data is defined as the “high volume, high velocity and/or high-variety of assets that demand cost-effective, innovative forms of information processing” providing important insights. Through music streaming, vasts amounts of music data are being outputted each day. This increased data volume is generated by subscribers of music streaming platforms. As consumers listen to music more via streaming platforms than any other format, this information is highly valuable and is increasingly directing the industry.
One area is music publishing, which is becoming more reliant on this data. Not only can big data share user behaviour trends, but it is also being used by A&R organisations to predict an artist’s probable success. Through analyzing song performance, particularly for new artists, it allows them to plan their budgets more intelligently.
Streaming platforms are providing more access to this data. At the end of last year, Spotify launched a beta version of Publisher Analytics, a tool they say will share real-time artist and album information.
Big data provides CMOs with valuable information on listeners and consumers, enabling the prediction of trends to help inform decisions. Despite having access to this valuable resource, big data is presenting challenges for organisations involved in royalty processing.
With an increased number of music streaming services, data can be highly unorganized and difficult to process. As this information is output from a plethora of sources and in many different formats, this poses a risk of bad metadata. Unorganized data, particularly at the volumes being generated by music streaming, is difficult to match. Without standardization of data formats, royalties can be difficult to process and pay to artists. This is why high performance matching is essential for CMOs.
CMOs can use its data to lead the industry. However an issue is the time sensitivity of music data, without effective and timely interpretation, it loses its value. As consumers listening behaviour is highly dynamic, information can become stale and unusable quickly. As the scale of data grows, a system capable of processing this volume in real time is essential.
Spotify echoes this through their “moments” approach. They analyse the users’ current behaviours and provide song suggestions and create playlists based on this. They can gather huge amounts of behaviour information from data such as time of day and location of the listener. The data forms into key insights such as what the listener is doing? Commuting? Studying? Music streaming is fully integrated with other platforms consumers use daily, from social media to their email accounts. Insights beyond music plays are garnered when the data is processed and connections are made. This creates a real time data tapestry of user behaviour which can help inform and direct the decisions.
For decision making, reliance alone on aggregated information can often lead to poor decision making and risk taking. It often does not consider external influences. With music streaming so dependent on user behaviour, external factors could affect information such as global economic factors or new competitors within the market. Decisions should be informed by big data analysis and not completely determined by it. Aggregated and analysed data combined with CMOs knowledge of their markets and expertise results in better informed decision making and trend prediction.
The cost of big data storage and processing is another issue. As Gartner states it requires “cost-effective” forms of processing. This relates also to music royalty processing. As repertoires grow in tandem with international music streaming increases, cloud based technologies can support CMOs.
Of course one of the major big data challenges for CMOs is in the name – it’s the sheer volume of data. As streaming continues to output massive amounts of metadata which needs to be processed, legacy systems are not equipped to manage. The Music Matching Engine is a high performance application that allows businesses to process data quickly and cost effectively, allowing CMOs not only distribute royalties but interpret the data to make future decisions.
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