The Music Creatives and Data Types

Only a few more days until the release of your most awaited music album. How will you get your hand on it? Will you purchase a vinyl record or CD of the album? How will you listen to it? Will you pop a cassette in or listen to it from your Zune mp3 player? The odds of you doing any of the options mentioned above is pretty unlikely. The reason? Technology.

Things change over time. Hip-hop/R&B music changed significantly over its history. In the same way, the course of time changed how people consume music. Music consumption has changed medium from vinyl records, radio broadcasting, cassette tapes, and Walkmans, to compact CDs, mp3s and USB drives, and finally, music downloading and streaming services. Advancements in technology have led us to an internet-focused means of music consumption and awareness where platforms like Pandora, Spotify, Apple Music, Soundcloud, and Shazam reign dominance. As a result, the music industry now uses a plethora of data to analyze all kinds of trends and insights.

Music industry big data is composed of different data types. One area of data pertains to information related to the user. User information is information about the customer consuming the music and how, when and where they are listening—what a consumer is doing when listening to music: studying exercising, driving to work, cooking, etc.; what time of day music was heard: morning, evening, afternoon, etc.; genre preferences; demographics such as location, age, gender; and social media accounts.

Another data type relates to the music itself. Companies like Spotify and Pandora employ this data to curate personalized streams and playlists for their customers. This kind of data are things such as the instruments included in a track; the gender of the vocalist; the style of backup vocals, the tempo of the rhythm, time signature, danceability and tone color; emotions the lyrics convey, the degree to which vocals are nasal-sounding; and sound effects.

The last type of data concerns the music creatives themselves. This type of information is "exported" instead of being "imported" as with user and music data. Shazam is a leader in this type of music data. Shazam is the first application to turn sound into data. "The smartphone app takes an acoustic fingerprint of song's sound to reveal the artist, sound title and album of the recording. When a user holds his phone toward a speaker playing a recording, he quickly learns what he is hearing" (The Conversation). Instead of collecting from the public, this data types are provided to the public to aid people in identifying songs they don't know.

The last data type concerning the creatives is a leading trend in modern music alongside SoundCloud Rap. The era of music we currently live in is called the Internet Era. As you can imagine, the overarching theme for this era is the Internet. Over the length of the past decade, a trend coined "Soundcloud Rap" emerged from the deepest pits of the music-internet relationship. SoundCloud is a free music streaming platform that allows users to upload, promote and share audio, and has become a prominent platform for new and upcoming music artists.

Recently, Shazam and other music industry services have used big datasets in predictive analyses to forecast upcoming trends. Music data suggests that there is a trend towards creating a narrower range of sounds—a tendency towards similarity. Using Pandora's Music Genome Project dataset, a dataset of sound descriptions and algorithms responsible for streaming personalization on Pandora, Andrew Thompson, and Matt Daniels conducted research that led them to discover this trend. They posted their findings on The Puddling (linked below).

New artists either follow the crowd or make their own way. Recently, according to The Guardian, Shazam has been able to identify new artists by looking at their data and other information:

Shazam combines critics' reviews alongside the number of people that have used Shazam to find a song to understand which artists are generating the most interest. This means that instead of only relying on what the criticism of the music has been, Shazam is able to use consumer behavior to better judge the artists that have already started to pique the interests of listeners and are starting to gain traction.

These days, reaching the limelight can be as simple as riding the wave or catching your own. On one side of the spectrum, upcoming artists can look at popular songs to curate their music in similar ways or with similar attributes in the hope of catching mainstream attention. Another way of rising to fame can be focus around authentic sound where an upcoming artist can hope to find fame by offering new sounds. An important platform for the latter is SoundCloud and its ability to give power back to the musicians and rappers as opposed to big record labels. The trends towards similarity can trace to the fact that more top songs are being produced by fewer people, "from 2010-2014, the top ten producers (by number of hits) wrote about 40% of songs that achieved #1 - #5 ranking on the Billboard Hot 100. In the late-80s, the top ten producers were credited with half as many hits, about 19%." Now that the internet has put power in the hand of unsigned artists with SoundCloud, maybe now we will enter a new era of Hip-Hop/R&B where the music industry begins to stray from conventional record label distribution and promotion.

One music industry factor that is of increasing importance is marketing. Big record labels are responsible for 80% of the market's output. As a result, we see a trend towards similarity in music. Record labels are a huge barrier for new artists looking to find commercial success because they hold most of the marketing know-how that music creatives need for successful careers. However, because current trends seem to be working to disempower said record companies, that power shifts towards the creatives. As datasets grow and marketing increases in popularity, it may become increasingly important for independent creatives to understand the business side of the music industry to establish themselves without a record label.

Sources:

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Albright, D. (2015, April 30). The Evolution of Music Consumption: How We Got Here. Retrieved October 28, 2018, from https://www.makeuseof.com/tag/the-evolution-of-music-consumption-how-we-got-here/

Datoo, S. (2013, December 10). How Shazam uses big data to predict music's next big artists. Retrieved October 28, 2018, from https://www.theguardian.com/technology/datablog/2013/dec/10/shazam-big-data-prediction-breakthrough-music-artists

Heitner, D. (2018, June 07). Big Data Is Revolutionizing the Music Industry. Here Are the Lessons for Your Business. Retrieved October 28, 2018, from https://www.inc.com/darren-heitner/big-data-is-revolutionizing-music-industry-here-are-lessons-for-your-business.html

Lucky, D. W. (2018, February 12). The Convergence of Big Data and the Music Industry – Cloud Computing Management – Medium. Retrieved October 28, 2018, from https://medium.com/cloud-computing-management/the-convergence-of-big-data-and-the-music-industry-cb54dc8c2968

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Rozenfeld, M. (n.d.). How Machine Learning Is Reinventing the Way We Discover Music. Retrieved October 28, 2018, from http://theinstitute.ieee.org/technology-topics/big-data/how-machine-learning-is-reinventing-the-way-we-discover-music

Seabrook, J. (2017, June 19). Spotify: Friend or Foe? Retrieved October 28, 2018, from https://www.newyorker.com/magazine/2014/11/24/revenue-streams

Thompson, A., Daniels, M., & Gaume, D. (n.d.). Are Hit Songs Becoming Less Musically Diverse? Retrieved October 28, 2018, from https://pudding.cool/2018/05/similarity/