Digital Streaming, Big Data, and Local Music: When Is There Enough Cowbell?

Running Head: DIGITAL STREAMING, BIG DATA, AND LOCAL MUSIC 1
Digital Streaming, Big Data, and Local Music: When Is There Enough Cowbell?
Why Less Difficult Does Not Equal “More Easy”
Alek R. Nybro
St. Edward’s University

DIGITAL STREAMING, BIG DATA, AND LOCAL MUSIC 2
Abstract
Digital streaming platforms were first introduced in the early 2000s. With this
introduction, the way we listen to and discover music was changed along with the trajectory of
the entire music industry. Given the exponential growth of the Internet, big data was becoming
more and more important. Streaming services began to use big data to develop music analytics
and I wanted to research its effect(s) on artists, specifically local/independent artists. As a Digital
Media Management undergraduate, I recognized this issue and its implications are of utmost
importance to my hometown of Austin, Texas.
Keywords: digital streaming platform, big data, music analytics, local/independent artists,
Digital Media Management
Before You Read
Big data is confusing enough as it is. This project approaches big data from a digital
media and music standpoint. I do not try and unravel the algorithmic or computational side, so
don’t minimize the tab quite yet, but I will be talking about the implications of the algorithms.
Below, I will briefly define a few keywords to review before reading to give you context…
● Digital streaming platform: services that allow you to stream music such as
Spotify, Shazam, Pandora, YouTube, SoundCloud, Tidal, etc. (I do not reference
all of these)
● Big data: a wide range of data collected from users’ interaction(s) on a digital
streaming platform; “huge collections of music-files and the interfaces through
which such content may be streamed or downloaded” (Koutsomichalis, 2016, p.
25)
● Music analytics: the field of understanding this big data and deriving meaning
from numbers to help artists, record labels, music industry professionals, etc.
● Local/independent artists: this is the term I use to refer to bands with origins in
my city

● Digital Media Management: program of study with an emphasis on digital
communication between business and entertainment (similar to digital marketing,
see link)
Introduction
Will Ferrell and Christopher Walken’s sketch is often considered as one of the greatest
Saturday Night Live sketches ever written and performed. If you are reading this and already
thinking, “More Cowbell,” then you would be correct. I remember seeing a 1970s era, rockabilly
Ferrell with excessive chest hair protruding out of his deep v-neck and Walken wearing
sunglasses inside (of course) with his greasy hair slicked back. The sketch is intended to portray
an overzealous cowbell player, Will Ferrell, and know-it-all music producer, Christopher
Walken, in the process of recording cowbell with a side of “(Don’t Fear) The Reaper.” It is based
off the “golden ear” phenomenon of producers before big data was even dreamt of; this
phenomenon was based merely on “purely subjective assumptions [that] would guide major
decisions” about “what people would want to listen to before they heard it” (Moon, 2017). By
the 1990s, record companies were incorporating “more market-based objective information
through focus groups, along with sheet music and record sales” (Moon, 2017). This trend of the
depersonalization of music analytics has continued to today with the use of big data. Now, big
data serves as the so-called “golden ears” of record companies. With the evolution of digital
streaming companies such as Spotify, Shazam, and Pandora, I knew that big data had to
influence the way we stream, listen to, and discover music to some extent—but to what extent?
This is what I wanted to answer through research not only as a student, but as a curious and
informed user.
As a Digital Media Management undergraduate, I will be required to address the ethical
implications of collecting, using, and transforming (personal) data. I have found a great deal of
interest in the field of big data and music analytics. However, I was not familiar with their
purpose(s) within the realm of digital streaming. To my surprise, the way digital streaming
platforms are using music analytics and big data is transforming the way we listen to and
discover music. Additionally, if I am a student studying in Austin, the proclaimed Live Music

Capital of the World, how do digital streaming platforms’ use of big data affect local music? To
understand the interplay between digital streaming platforms and big data algorithms, we first
need to understand how music analytics is capable of potentially picking a “hit” song.
For example, Next Big Sound, a music analytics and insights company, successfully
predicted the breakout of OMI’s 2015 hit song “Cheerleader.” Brian Moon, Assistant Professor
of Music at University of Arizona, poses an existential question for the music industry based on
this prediction. In his article “How Data Is Transforming the Music Industry,” Moon asks if
listeners and producers even have a chance for music to resonate, asking, “Does taste even
matter?” Moon’s existential question actually raises another question: Did people like
“Cheerleader” because of its sound and social media buzz or did the song become popularized
because it possessed the characteristic traits of a successful record (Moon, 2017)? Even as early
as 2002, Hit Song Science, a product of Barcelona-based Polyphonic HMI, accurately predicted
that Norah Jones’ album, Come Away With Me, would be a major success despite the bickering
among many industry insiders (Blumenfeld, 2016). And to this day, mid-range clothing retailers
cannot seem to get enough of it.
Through the 1990s, the music industry was evolving how it predicted and promoted its
hits; alongside these developments were changes in how individual listeners thought about what
made up music and what drove their choices in what they listened to. In 1999, the Music
Genome Project paved the way for the music analytics industry through categorizing songs based
on their acoustic properties. The company later changed their name to Pandora in the mid-2000s.
However, it was more than a mere “name change.” Whereas the Music Genome Project served
as a music recommendation service for brick-and-mortar stores, Pandora geared its product
towards the consumer and “created a highly marketable music recommendation engine that laid
the foundation for streaming services to take over the music industry” (Blumenfeld, 2016).
Furthermore, this served as the “one ring to rule them all” in music industry terms—big data 1,
Blue Öyster Cult 0. This brings us back to the music industry’s existential question: Does taste
even matter? Do we listen to what we enjoy or what the data predicts we will enjoy? Will this
feedback loop shape what we are listening to right now based on what we listened to in the past?
And is this data powerful enough to change what we will enjoy in the future?

There is also an emerging coevolution between streaming and live music. Rather than
lining up at Columbia Records to buy a new album, most listeners open the digital streaming app
of their choice, and enjoy hours of ad-free music, so long as they pay a monthly fee. However,
more and more, the “ads” played aren’t for other products but for bands themselves. Scholars at a
University in Finland have discovered that “the parallel paths of increasing popularity of
streaming services and a resurgence of live music” suggest that these two dynamics are working
together toward a more sustainable music industry (Naveed et al., 2017, p. 6). Spotify has
spearheaded this movement through using algorithms to not only generate curated playlists but
also concert recommendations (Blumenfeld, 2016). As a student in Austin, Texas, I don’t always
think of concerts as 25,000 people in a single arena. Instead, I think of intimate venues filled
with sweaty fans such as The Parish on 6th or The Mohawk on Red River.
When I think of live music, I think of local music. Local bands tend to make the majority
of their profit from live shows because their music doesn’t receive as much exposure on digital
streaming platforms. With the progression of the Internet comes a myriad of ways to share
music. The more these ways can be tracked “by a label’s A&R department, a management firm,
or a corporate brand,” the better chance they have of discovering an independent/local artist who
is making a splash on one particular platform (Blumenfeld, 2016). It is common practice for
music industry professionals and brand partners to consult these music analytics regularly, but it
is also appropriate for independent/local artists to use these services as a way to discover where
and how their listeners are best interacting with their music. To understand this relationship
between algorithms and local musicians, I attempted to bridge the gap of conversation by
conducting interviews with music industry veterans (music journalists, music tech founders,
local bands) within the Austin area.
Discussion
Big Data Influencing Revenue
Unfortunately, all bands were not created equal under the one nation of big data. This is
where Senator Bernie Sanders clenches his fists and says “across all services, more than 99
percent of streams last year came from just the top 10 percent most-streamed tracks overall”

(Hogan, 2018). Our white-haired Vermonter is not wrong. It is not the Justin Biebers or Kendrick
Lamars of the music industry that are “feeling the bern”—it is the local/independent artists. And
an even more harrowing statistic? “The top 1 percent of bands and solo artists now earn 77
percent of all revenue from recorded music” (Thompson, 2014). OK, but local/independent
artists have to earn a little something too, right? Nope. A local band I interviewed, Shadow of
Whales, sometimes earns “just $5 a month” from streaming revenue on Spotify. Another local
band, Sure, says bands “would need serious leverage in the music industry to monetize streaming
past the point of having two nickels to rub together” (B. Nybro, email interview, April 7, 2018).
Let’s return to Kendrick Lamar though. When he dropped his album, To Pimp a Butterfly, he
“made between $921,600 and $1,290,240 in twenty-four hours” from streaming revenue on
Spotify (Sheffield, 2015). With streaming making up more than half of the industry’s revenue,
there are several other services besides Spotify from which a band can gain streaming revenue;
however, Spotify has more users and paying subscribers than any other streaming service
(Hogan, 2018). When you think it could not become any more difficult for local/independent
artists to turn a penny on streaming, it does. Spotify pays $0.0038 per stream to unsigned artists
which means they would need 380,000 plays to earn minimum wage (Sanchez, 2017).
Despite “the gap between ‘Big Label’ and ‘Independent’ artists,” Austin music tech
founder, Nathalie Phan, believes “that with the right management and the data they have already
collected (specifically the regional and geographically based data), Spotify and other streaming
services [will be] able to help promote smaller indie artists” in terms of proper streaming
compensation (N. Phan, email interview, March 21, 2018). Surprisingly enough, there is still a
glimmer of hope. With Spotify going public in March 2018, there will likely be some aspects of
added financial transparency. Underpaid artists should have more reason and verified evidence to
object to their unfair compensation. But even with greater transparency from companies, there is
still likely going to be a lack of trust in the relationship between artists and streaming services.
Today, many artists see themselves as being “too reliant” and “unfairly compensated” by digital
streaming services so they have “shifted their focus towards concert tours as their primary source
of income” (Naveed et al., 2017, p. 2).

Big Data Influencing Live Music
With the increasing popularity of streaming services, the music industry has experienced
a resurgence of live music (Naveed et al., 2017, p. 3). Proposals have been made at Spotify to
“[sell] data to live concert companies” (Hogan, 2018), a move which could offer concert
promoters, particularly those who “study Spotify listens to route tours through towns with the
most fans,” more accurate data which could translate into more concerts in towns with higher
listening and interaction activity (Thompson, 2014). This could even mean more revenue from
ticketing and exposure for local bands like Shadow of Whales who front load themselves with a
bunch of gigs (J. Boyum, email interview, March 26, 2018). Sure also believes that “Spotify is
much better at getting [the band] recognition, which can then be translated into ticket [and]
merchandise sales” (B. Nybro, email interview, April 7, 2018). The band also suggests that
streaming isn’t the only option available to them; they noted that “Spotify is only good for
getting bodies at shows, and there are so many other, more effective ways for local artists to do
that than to hope that someone from their city chances on their music on the largest music
streaming platform in the world” (B. Nybro, email interview, April 7, 2018). Spotify is a great
tool for building fan bases that span across the world. In theory, worldwide reach seems like the
dream of all indie bands, but this only sounds good until you consider that “for a small, local
band, [it] means a handful of people scattered across continents that you will likely never make it
to” (B. Nybro, email interview, April 7, 2018).
Although underpaid artists on streaming services are not seeing a desirable return on their
investment (in terms of ticketing revenue), scholars have gone as far to say that big data has
“transformed the live music industry into a ‘live-concert-streaming music industry’” (Naveed et
al., 2017, p. 1). Since every other format of the recorded music industry is declining, streaming
has the power and capability to be the driving force behind live music (Naveed et al., 2017, p. 3).
This relatively recent shift has the potential to further the acceleration of our high dependency on
live music while maintaining streaming as an accessory due to its advantages in accessibility and
portability (Naveed et al., 2017, p. 3). Now, artists will find the most success in promoting their
music through streaming services and by conducting live tours. This raises an implication—is the

music of local/independent artists as easily discovered on streaming services as larger, more
renown artists?
Big Data Influencing Discovery and Listening
Why did Kendrick Lamar make close to one million dollars in streaming revenue within
twenty-four hours of releasing To Pimp a Butterfly when some local bands only make five
dollars a month in streaming revenue? The payment per play ratio aside, his music was made
easily discoverable and available on these large streaming services. Free Press Houston music
journalist, Russel Gardin, offers the most succinct explanation for this: “streaming services will
present A-list artists to the users in a way that is much more convenient and accessible than local
bands” because they bring in more profits (R. Gardin, email interview, March 19, 2018). This
could include making these A-list artists more widely played on features such as Spotify Radio
and therefore more listened to by users.
Spotify Radio, one of Spotify’s many features, allows users to find new music within the
service’s storage catalogue. However, the radio feature has been disliked and accused by many
for playing the same artists over and over (Snickars, 2017, p. 184). Scholars at a university in
Sweden wanted to find out why “algorithmic music discovery today features and promotes some
artists and simply ignores others” (Snickars, 2017, p. 185). They went about this through reverse
engineering Spotify’s algorithms to break into the secret infrastructure of digital music
distribution. The scholars programmed two rounds of bots to simulate listeners on the radio. The
first round of bots started a Spotify Radio station based on the highly popular ABBA song,
“Dancing Queen” (released in 1976, with some 65 million streams on Spotify); the second round
of bots started a Spotify Radio station based on the significantly less popular Swedish
progressive rock band, Råg i Ryggen’s song, “Queen of Darkness” (released in 1975, with
approximately 10,000 streams on Spotify) (Snickars, 2017, p. 203). As for the results of this
reverse engineering, the ABBA radio station “recommended artists that were strikingly similar,
belonging to a homogenous genre of popular hit music from the 1980s,” while the “Queen of
Darkness” radio station played “a much greater variety in terms of artists and songs, and
importantly so also from other periods than the 1970s” (Snickars, 2017, p. 200-201). This reverse
engineering proved to an extent that popularity and homogeneity could go hand in hand when it

comes to discovery features like Spotify Radio. Derek Thompson alludes to this concept in “The
Shazam Effect,” “if you give people too much say, they will ask for the same familiar sounds on
an endless loop, entrenching music that is repetitive, derivative, and relentlessly played out”
(Thompson, 2014). In the case for Spotify Radio, is this the expertise of big data accommodating
to our listening tendencies as users?
In an experiment regarding online rankings for songs, some sites displayed a song’s true
download count and others showed “inverted rankings, where the least-popular song was listed
in the No. 1 spot” (Thompson, 2014). The inverted rankings caused previously neglected songs
to suddenly become popular, and previously popular songs to suddenly become neglected. This
would be equivalent to replacing Drake’s No. 1 song, “God’s Plan,” with Shadow of Whales’
song, “Runaway,” on Billboard’s Hot 100 chart. What isn’t clear from the study is if the belief
of artificial popularity caused users in this experiment to listen to what they like or if they were
motivated to just listen to what was popular, but is seems that—“simply believing, even wrongly,
that a song was popular made participants more likely to download it” (Thompson, 2014). This
brings us back to the music industry’s existential question: Does taste even matter? While fans
can burrow deep into “rabbit holes of esoterica, ‘Today’s Top Hits’ is still the No. 1 playlist on
Spotify, and Pandora’s most popular station is ‘Today’s Hits’” (Thompson, 2014). Even in a
universe filled with incredibly diverse music, most of us revert to listening to what we see
everyone else streaming.
Big Data Influencing Discovery and Listening Locally
Despite the controversy surrounding the capability of big data algorithms to guide our
taste and discovery of music, Shadow of Whales still maintains a positive outlook on the role big
data can play in the discovery of their music by new listeners across digital streaming platforms.
They said “one of the first questions people ask us after meeting us or seeing us in concert [is if
we’re on Spotify]” (J. Boyum, email interview, March 26, 2018). They went on to speak to
Spotify’s algorithms for recommending new music, noting the complexity of relationships
between small and large bands, indie and mainstream: “the more fans that we gain of other larger
bands fans, the more likely Spotify is to recommend us to more fans like them via Spotify Radio
and the more likely we are to get picked up on a larger playlist” (J. Boyum, email interview,

March 26, 2018). What we can hear Shadow of Whales referring to is the “social media
phenomenon [that has] contributed to the growth of the fan base, allowing rising artists to easily
connect through new digital marketing techniques [from] already established acts” (Naveed et
al., 2017, p. 3). Gardin views these algorithms as “giant companies that essentially [tell] us what
we [are] going to like before we [decide] we [do] in fact like it” (R. Gardin, email interview,
March 19, 2018). He goes on to state how these algorithms might disenfranchise local bands
because they “are guiding a blind audience towards what is considered a ‘good’ or ‘popular’
song” (R. Gardin, email interview, March 19, 2018). The downside of this approach, as Gardin
says, is “bands that are truly innovative and doing something unique stand no real chance at
breaking into the big leagues” (R. Gardin, email interview, March 19, 2018).
Even with backlash and negative connotations of big data algorithms, Shazam is
accomplishing great feats for some local/independent artists through them. Shazam possesses the
power of identifying which songs are gaining in popularity in certain geographic locations by
studying 20 million searches every day (Thompson, 2014). Lorde was first discovered by
Shazam in 2013 when the searches of her song, “Royals,” spread from New Zealand to Nashville
and then to over 3,000 U.S. cities the next day (Thompson, 2014). An example on a more micro
level is with R&B singer, SoMo, from Denison, Texas. A radio station in Victoria, Texas
(outside of Houston, Texas), had started playing SoMo’s song, “Ride.” Even though “a town of
just 63,000 won’t launch a national hit by itself,” “‘Ride’ [was] the No. 1 tagged song in
Victoria” on Shazam’s interactive discovery map (Thompson, 2014). Returning to Austin,
Shadow of Whales spoke to how Shazam has allowed “a lot of people to connect with [them]
when they hear [their] music in stores, restaurants, or other businesses” (J. Boyum, email
interview, March 26, 2018). Shazam allows listeners, artists, and record labels to see where a
song is trending no matter if it is in Victoria, Texas, or New York City. But Shazam is incapable
of processing anything besides pre-recorded music; shazaming tracks when you are at a concert
or music festival is out of the picture. This is why it is not as easy for some local artists to
maintain a positive outlook on the way big data is affecting their audiences. Sure states that
Shazam “only works for recording artists” (B. Nybro, email interview, April 7, 2018). Local

music in Austin tends to be consumed through live performances so Sure does not see Shazam
benefitting smaller, local artists (B. Nybro, email interview, April 7, 2018).
Conclusion
Digital media entrepreneur, Jeremy Silver contends “these are very early days for big
data in music” (Silver, 2015). Big data still has a big contribution to make, and in some ways,
Silver echoes exactly what Phan predicted, if we read their words side by side:
In the future the combination of computer analytics and social science will undoubtedly
reveal even more powerful ways of targeting music to receptive fans. I suspect that a lot
more big data will flow through the digital gateways before the industry fills the skills
gap, which currently prevents it from realising the real benefits data science can bring to
the industry. (Silver, 2015)
While we can use algorithms to curate pretty spot-on music recommendations to different
individuals using historical music trends with different demographics, populations, and
individuals, we can surely go beyond that level and analyze correlations between types of
music played and where they are being played, categorized by age groups and income,
and interests of specific demographics. There's a lot of power in data that I don't think we
utilize (or haven't yet). (N. Phan, email interview, March 21, 2018)
There still exist many areas of improvement for big data and digital streaming services in
the eyes of local bands. Sure created a wish list of sorts for the future of platforms like Spotify.
For example, they want streaming platforms to value location and place, making it clearer where
the artist is from, not just to promote local artists to local citizens but to help listeners in the area
discover “other artists from that same ‘scene’ (similar genre, same city).” This would benefit
local artists by allowing them to piggyback off of larger artists and each other, as well as
exposing users to different types of music. Sure would also like to see “an interactive map of the
world” that allows users to browse by genre or zoom in and view top artists from different cities.
The band also expressed a desire for more control such as “editing [Spotify’s] Related Artists”
and “allowing for more expansive merchandising and ticketing widgets on the artist page” (B.
Nybro, email interview, April 7, 2018).

Will streaming services be able to leverage these untapped powers to empower
local/independent artists? Or will big label, A-list artists continue to reap the benefits of music
analytics and big data? These questions largely depend on the ethicality of big data algorithms
that companies like Spotify, Pandora, and Shazam employ. As a Digital Media Management
undergraduate at a liberal arts university in Austin, Texas, it only makes sense that I conclude my
research by relating the ethicality of these algorithms to local music. This highlights the general
knowledge that undergraduates like me need to have of issues that fall under mathematics,
computer science, and data analytics. More importantly, this highlights the awareness that we
need to have of our surrounding community and how the fields we are studying affect the area
which we call home.

References
Blumenfeld, Z. (2016, February 11). A Trillion Data Points: The Growth Of Music Analytics.
Retrieved April 06, 2018, from
http://performermag.com/band-management/a-trillion-data-points-the-growth-of-music-a
nalytics/
Boyum, J. (2018, March 26). Email interview.
Digital Media Management. (n.d.). Retrieved April 06, 2018, from
https://www.stedwards.edu/undergraduate/digital-media-management
Gardin, R. (2018, March 19). Email interview.
Hogan, M. (2018, January 08). What Spotify Going Public Could Mean for Music Fans.
Retrieved April 06, 2018, from
https://pitchfork.com/thepitch/what-spotify-going-public-could-mean-for-music-fans/
Koutsomichalis, M. (2016). From music to big music: Listening in the age of big data. Leonardo
Music Journal, 26, 24-27.
Moon, B. (2017, May 21). How data is transforming the music industry. Retrieved April 06,
2018, from
http://theconversation.com/how-data-is-transforming-the-music-industry-70940
Naveed, Watanabe, & Neittaanmäki. (2017). Co-evolution between streaming and live music
leads a way to the sustainable growth of music industry – Lessons from the US
experiences. Technology in Society, 50, 1-19.
Nybro, B. (2018, April 7). Email interview.
Phan, N. (2018, March 21). Email interview.
Sanchez, D. (2017, July 24). What Streaming Music Services Pay (Updated for 2017). Retrieved
April 06, 2018, from
https://www.digitalmusicnews.com/2017/07/24/what-streaming-music-services-pay-upda
ted-for-2017/
Sheffield, M. (2015, November 20). How much money did Kendrick Lamar make on Spotify

yesterday? Retrieved April 06, 2018, from
http://www.hopesandfears.com/hopes/now/pop-stuff/168737-how-much-money-did-kend
rick-lamar-make-on-spotify-yesterday
Silver, J. (2015). These are Early Days for Big Data in Music. Music Week, 21.
Snickars, P. (2017). More of the Same – On Spotify Radio. Culture Unbound,9(2), 184-211.
doi:10.3384/cu.2000.1525.1792
Thompson, D. (2014, November 29). The Shazam Effect. Retrieved April 06, 2018, from
https://www.theatlantic.com/magazine/archive/2014/12/the-shazam-effect/382237/

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