Big Data and Independent Artists: Insights from Brazilian Funk and Sertanejo

In the early 2010s, the concept of being an ‘independent artist’ boomed, with many artists relieving themselves from the commercial expectations that came with being signed to major record labels and exploring their creative freedom with respect to their writing and production on their own.

Independent artists in today’s digital environment, perhaps, feel even freer, as they have more room for experimentation. For example, they can post short-form videos featuring their upcoming releases and evaluate which performs best, using that as an indicator to choose which songs should be released as singles. They can also use social media analytics to determine where their fans are located, helping them decide on the cities to book their next tour. However, what might be perceived as “freedom of choice” to an artist nowadays, might be closer to manipulation if artists and other music industry professionals are not trained to interpret their data.

 While commercially released music has been a commodity ever since it has been a source of profit[1], it was previously a cultural product — of which plastic copies were commercialized. This has been replaced by digital content, and its target audience is no longer fans who actively purchase it, but fans who listen to it and generate aggregated revenue like streams, ad clicks, and publicity fees[2].

 Cultural industries are increasingly mediated by privately owned digital platforms (e.g. Meta, Google, Apple, Amazon). These platforms’ economic and infrastructural extensions affect not only distribution but also the production of culture itself[3]. While algorithmic logic has been affecting the production of video games, the formatting of news, and society itself[4], it is increasingly shaping music and the arts, which are beginning to depend on algorithmically filtered platforms for their commercial success[5].

Success in the streaming era seems to be measured by followers, listeners, and streams as if they were objective indicators of success. We can parallel this to how a song’s success was measured — generally, in a biased manner — in the 20th century: Billboard’s Top 40[6] or sales reports from physical stores like SoundScan[7]. Today, it is measured by digital analytics programs from all sorts of platforms: Spotify for Artists, Instagram Insights, Google and YouTube Analytics, among many others. As biased as major label marketing executives were in the 20th century, most of them had at least some kind of knowledge of data interpretation. Nowadays, however, independent artists, producers, and various music industry executives are drowning in an ocean of valuable data, with little ability to make use of it.

This knowledge gap leads to poor decision-making. We have seen booking agents book artists for local venues based on their large following on socials, ignoring the fact that those followers might live in a different country; A&R executives curating festival line-ups based on an artist’s monthly Spotify streams, being unaware that a large portion of those streams actually come from passive listeners on editorial playlists, and not legitimate fans. Some A&R executives might even select an artist based on the number of YouTube views of a music festival’s full live stream, not considering how many viewers were retained during that artist’s set, since a lot of times, viewers will join the live stream only to watch a specific artist and then log out.

The shallow use of data from industry professionals such as booking agents, A&R executives, and many others, is extremely harmful to artists. However, it is evident that artists themselves are manipulated into producing their music following this same logic. For a while now, Spotify for Artists’ main promotional video has been “How to get playlisted”[8]. While several trolls and hackers have dribbled past the system to increase their revenue through ambient soundtrack playlists[9], independent artists are buying into the frenzy of getting playlisted as the main goal of a new release.

This isn’t new. Just like how radio and print media acted as “gatekeepers” and had a major influence on the growth of artists by the end of the 20th century[10], playlist curators have the power to put new songs and artists in the spotlight[11]; except, many playlists are curated by algorithms instead of humans. A song will only stay on an editorial playlist as long as it performs well within that playlist in the first week[12]. Algorithmic playlists such as “Discover Weekly” and “Release Radar” completely skip human curation. They are built upon several “taste” indicators, including tracks the user has previously listened to, artists and playlists they like and/or follow, and artists and tracks liked by their friends. Each interaction made by a user on a platform generates data that can be used for this kind of tracking. Plays, likes and follows on a music platform are registered and built up to this profile, as well as any other interactions on associated social networks like Facebook, Instagram, and TikTok. By crossing this information with each song’s individual data — such as genre tags, the vocalist’s gender, the band’s formation, samples, tempo and even “danceability” — the algorithm selects tracks that are most likely to interest listeners.

This model fits the concept of a datacracy: a system in which decisions are data-driven[13]. “Datacracies” are applied to virtual territories, which are not bound to geopolitical laws, but instead, to virtually organized communities[14].

Independent artists, while trying to grasp the new “datacratic” market and understand the algorithm’s process of choosing songs for playlists, find themselves with a mixture of biased goals for each new single: getting playlisted, and getting online fans engaged. Unfortunately, there is no formula for this — although various music business and social media “experts” might argue it exists and offer standardized solutions through their online courses. Social media influencers have been reverse engineering the algorithmic black boxes of their chosen platforms for a while now, by tailoring their communication strategies based on their own results[15]. Musical artists, however, might be tailoring the essence of their art in pursuit of a biased quantitative digital status, moving against their owned independence from majors[16].

Previous research has indicated that platforms themselves behave differently for each musical genre[17]. This research is based on the Brazilian music market, which is very peculiar. While Brazil is one of the fastest growing music markets — due to the growth of music streaming[18] — it is also one of the few countries in the world in which its national music completely dominates the charts and general consumption[19]. Of the top 100 songs in Brazil on streaming platforms in 2021, only 6 were non-Brazilian; the first international song to appear in this list was Lil Nas X’s “Montero (Call me by your name)”, in 42nd place[20]. The other 94 were distributed between two of the most characteristic genres of Brazilian popular music: Sertanejo and Brazilian Funk.

Sertanejo is a form of Brazilian folk music that originated in the rural areas of states like São Paulo and Minas Gerais — though, today, it carries various pop elements. It can be compared to the sonority and general “vibe” of American country music and what it represents. Brazilian Funk, on the other hand, has elements such as sticky ostinatos, lyrical puns, and short melodies[21]. While both genres are associated with partying, they navigate digital music and media platforms in completely different ways. This is evidenced by a study through a Digital Music Gatekeeping model, with data provided by the Brazilian aggregator Playax, which will be explored in the paragraphs below.

Payola is not a forbidden practice in Brazil. It began as a few informal gifts and incentives but eventually turned into huge sums of money being used to make producers’ ways into radio programmers’ taste[22]. Now, it’s become as common, legal, and essential as advertising — included as promotion fees. This practice is at the root of the promotion of Sertanejo tracks and banning it would completely alter the Brazilian music market. When studying data from Sertanejo artists in 2018[23], it became clear that the primary method of reaching Sertanejo fans was through the radio, and most of the radio plays come from these payola incentives; for a few weeks, the track is played extensively, and then suddenly forgotten.

Brazilian Funk, however, has a completely different entry point into the gatekeeping circle. All of the Brazilian Funk tracks from the study mentioned above made their first appearance on YouTube, and most times, as home recordings from independent artists. Re-recordings of both audio and video with the famous funk producer KondZilla would then boost them to stratospheric numbers on YouTube, such as “MC Loma e as Gêmeas Lacração”, which grew from 500,000 to 3 million daily views (both averages) with the re-release of their track “Envolvimento” by KondZilla’s label and YouTube channel.

One thing the artists that have been placed into the 2021 Brazil top 100 songs (mentioned previously) have in common is an organized marketing department and the acquired knowledge of their main gatekeeping entry points. Brazilian Funk artists are investing in YouTube, while Sertanejo artists are investing in radio. Playlisting does not have the same effect on their success, which is shown by their lower Spotify streams when compared to YouTube and radio at the time.

Out of that same 2021 Brazilian top 100 list, 26 tracks were by independent artists, and 4 of them were by a Brazilian Funk alternative label. All other 70 tracks were listed as distributed by major labels, although many of them might have only distribution and not recording contracts. Be it independent, distributed, major contracted or alternatively released, these artists produce music in such characteristic genres that they are able to identify genre-specific paths that work for their particular career.

This leads us back to the independent artists that don’t have a marketing department, and that fit into less peculiar genres than Sertanejo or Brazilian Funk. It is much harder for an independent artist to identify if they should invest their time and money on YouTube, Instagram, Spotify, radio, or TikTok, and how to create a balance between so many options.

Even when an artist is successful in a specific digital space — such as Instagram or TikTok — would their followers (who were probably attracted to their homemade, intimate acapella covers) attend their full band set of original music in a local venue?

Gathering and analyzing music-related data is at the core of Playax[24], the Brazilian company which provided the base data for the 2018 analysis of the Sertanejo and Funk markets. Their service began by monitoring radio, “listening” to different stations, identifying tracks by audio fingerprint, and then providing detailed reports on radio plays for artists, who would then coordinate tours with local radio campaigns. The majority of their clients are large media conglomerates and concert organizers, interested in using data intelligence to select their acts. Independent artists, however, could be the most benefited by this model of data service, which helps analyze and interpret large amounts of data rather than just providing artists with raw data; since it can be misinterpreted and might even affect one’s creative decisions.

Big data is here, and is reachable, but it is drowning artists in a sea of misinformation[25]. Knowing how to make sense of it is essential, or else artists might end up producing music to fit into platforms that aren’t even used by their target audience. Navigating data without understanding it can transform the freedom and autonomy gained by independent artists into vulnerability and discouragement, an even bigger manipulation and sense of pressure than the historic effect major labels had on their artist’s creative process.

Edited by Tavishi Nidadhavolu


[1]Straw, Will. 2002. “Music as Commodity and Material Culture.” Repercussions 7–8: 147–71.

[2]Gurgel, Dani. 2016. “O Novo Público Da Indústria Musical : Aquele Que Compra Ou Aquele Que Escuta?” Signos Do Consumo 8 (2): 44–53.

[3]  Nieborg, David B., and Thomas Poell. 2018. “The Platformization of Cultural Production: Theorizing the Contingent Cultural Commodity.” New Media and Society 20 (11): 4275–92.

[4]Beer, David. 2017. “The Social Power of Algorithms.” Information Communication and Society 20 (1): 1–13.

[5]Navar-Gill, Annemarie. 2020. “The Golden Ratio of Algorithms to Artists? Streaming Services and the Platformization of Creativity in American Television Production.” Social Media and Society 6 (3).

[6]Hirsch, Paul M. 1972. “Processing Fads and Fashions : An Organization-Set Analysis of Cultural Industry Systems.” The American Journal of Sociology 77 (4): 639–59.

[7]McCourt, Tom, and Eric Rothenbuhler. 1997. “SoundScan and the Consolidation of Control in the Popular Music Industry.” Media Culture & Society 19: 201–18.

[8]Available at: Accessed in Jun 22 2022.

[9]Morris, Jeremy Wade. 2020. “Music Platforms and the Optimization of Culture.” Social Media and Society 6 (3).

[10]Shoemaker, Pamela, and Tim Vos. 2009. Gatekeeping Theory. New York: Routledge.

[11]Prey, Robert. 2020. “Locating Power in Platformization: Music Streaming Playlists and Curatorial Power.” Social Media + Society 6 (2): 205630512093329.

[12]Bonini, Tiziano, and Alessandro Gandini. 2019. “‘First Week Is Editorial, Second Week Is Algorithmic’: Platform Gatekeepers and the Platformization of Music Curation.” Social Media and Society 5 (4).

[13]Gurgel, Dani. 2021. “Datacracia Na Indústria Musical: Quando Todos Somos Gatekeepers e Produzimos Música Como as Plataformas Pedem.” In ABCiber.

[14]Lemos, André. 2009. “Cibercultura Como Território Recombinante.” In A Cibercultura e Seu Espelho: Campo de Conhecimento Emergente e Nova Vivência Humana Na Era Da Imersão Interativa., edited by E TRIVINO and E CAZELOTO, 38–46. São Paulo: ABCiber; Instituto Itaú Cultural.

[15]Brevini, Benedetta, and Frank Pasquale. 2020. “Revisiting the Black Box Society by Rethinking the Political Economy of Big Data.” Big Data & Society 7 (2): 205395172093514.

Petre, Caitlin, Brooke Erin Duffy, and Emily Hund. 2019. “‘Gaming the System’: Platform Paternalism and the Politics of Algorithmic Visibility.” Social Media + Society 5 (4): 205630511987999.

Cotter, Kelley. 2019. “Playing the Visibility Game: How Digital Influencers and Algorithms Negotiate Influence on Instagram.” New Media and Society 21 (4): 895–913.

[16]O’Dair, Marcus, and Andrew Fry. 2020. “Beyond the Black Box in Music Streaming: The Impact of Recommendation Systems upon Artists.” Popular Communication 18 (1): 1–13.

Eriksson, Maria, Rasmus Fleischer, Anna Johansson, Pelle Snickars, and Patrick Vonderau. 2019. Spotify Teardown: Inside the Black Box of Streaming Music. Cambridge: MIT Press.

[17]Gurgel, Dani, Luli Radfahrer, Alexandre Regattieri Bessa, Daniel Torres Guinezi, and Daniel Cukier. 2020. “Digital Music Gatekeeping: A Study on the Impact of Spotify Playlists and YouTube Channels on the Brazilian Music Industry.” In Spotification of Popular Culture in the Field of Popular Communication, edited by Patrick Burkart, 78–99. New York: Routledge.

[18]IFPI. 2019. “Global Music Report 2019: State of the Industry.” Zurich.

———. 2021. “Global Music Report 2021.” Zurich.

ProMusicaBR. 2022. “Mercado Brasileiro Em 2021.”

[19]Wikström, Patrik. 2009. The Music Industry: Music in the Cloud. Cambridge: Polity Press.

[20]ProMusicaBR. “Mercado Brasileiro Em 2021”.

[21]Sá, Simone Pereira de, and Gabriela Miranda. 2011. “Aspectos Da Economia Musical Popular No Brasil: O Circuito Do Funk Carioca.” In Nas Bordas e Fora Do Mainstream Musical: Novas Tendências Da Música Independente No Início Do Século XXI, edited by Micael Herschmann. São Paulo: Estação das Letras e Cores.

[22]Gambaro, Daniel, Eduardo Vicente, and Thais Saraiva Ramos. 2018. “A Divulgação Musical No Rádio Brasileiro: Da ‘Caitituagem’ Aos Desafios Da Concorrência Digital.” Revista Contracampo 37 (2).

[23]Gurgel et al. “Digital Music Gatekeeping”

[24]Available at: Accessed in Jun 22 2022.

[25]Sadowski, Jathan. 2019. “When Data Is Capital: Datafication, Accumulation, and Extraction.” Big Data and Society 6 (1).

Brevini, Benedetta, and Frank Pasquale. 2020. “Revisiting the Black Box Society by Rethinking the Political Economy of Big Data.” Big Data & Society 7 (2): 205395172093514.

O’Neil, Cathy. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown Publishing Group.



Leave a Reply

Your email address will not be published. Required fields are marked *