Big Data y televisión. Una reflexión crítica sobre el auge del Big Data como nuevo paradigma tecno-económico, y su impacto en el concepto de target de audiencia


Este artículo explora el estado de la cuestión sobre los desafíos y oportunidades del Big Data para incre-mentar el valor de las relaciones entre los operadores de televisión, las audiencias y los anunciantes que permiten los servicios digitalizados de televisión. Se plantea que la investigación sobre Big Data requiere prestar mayor atención a cuestiones críticas en las ciencias sociales y en la cultura –relacionadas con la comunicación y la gestión de medios– para ayudarnos a comprender que el Big Data puede, perfectamen-te, encajar en el paradigma tecno-económico dominante; una meta-narrativa sobre una revolución tecno-lógica sustancial que tiene el poder de transformar todos los ámbitos: cuando se difunde, multiplica su im-pacto en la economía y, finalmente, modifica las estructuras sociales e institucionales. Aunque es legítimo e importante preguntarse cómo el Big Data proporciona valor a las decisiones estratégicas de los operado-res de televisión, conviene mantener el escepticismo sobre lo que se puede obtener del Big Data para los servicios de televisión mientras las cuestiones socio-culturales no se resuelvan. Hay que analizar con senti-do crítico las estrategias de mercantilización de la audiencia o de target de audiencia, mediante las que sus datos se venden como una simple mercancía a los operadores y anunciantes.
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Altimeter. (2013). The converged media imperative: How brands must combine. Paid, owned, and earned media.

Altimeter. (2014). Data everywhere: Lessons from big data in the television industry (by Susan Etlinger).

Amit, R., & Zott, C. (2012). Creating Value through Business Model Innovation. Sloan Management Review, 53(3), 41-49.

Anderson, C. (2009). The longer Long Tail: How endless choice is creating unlimited demand (updated and ex-panded edition). London, UK: Random House Business Books.

Arsenault, A. H. (2017). The datafication of media: Big data and the media industries International Journal of Media & Cultural Politics, 13(1-2), 7-24. doi:

Askwith, I. D. (2007). Television 2.0: Reconceptualizing TV as an engagement medium.

Bateson, G. (1951). Communication: The Social Matrix of Psychiatry. New York, W.W. Norton.

Baumann, S., Hasenpusch, T. C. (2016). Multi-Platform Television and Business Models: A Babylonian Clutter of Definitions and Concepts. Westminster Papers in Communication and Culture, 11(1), 85-102.

Bobineau, J. (2014). SaveWalterWhite.Com: Audience Engagement als Erweiterung der Diegese in Breaking Bad. In J. Nesselhauf, & M. Schleich (Eds.), Quality-TV: Die narrative Spielwiese des 21. Jahrhunderts?! (pp. 227-240). Berlin: Lit-Verlag.

Boyd, D., & Crawford, K. (2011). Six provocations for big data: SSRN Scholarly Paper No. ID 1926431, Rochester, NY: Social Science Research Network,

Brown, I. (2016). The economics of privacy, data protection and surveillance. In M. Latzer & J. M. Bauer (Eds.), Handbook on the economics of the Internet (pp. 247-262). Cheltenham and Northhampton, UK: Edward Elgar Publishing.

Bughin, J. (2016). Big data, Big bang? Journal of Big Data, 3(2). doi:

Bughin, J., Byers A. H., & Chui, M. (2016). How social technologies are extending the organization.

Buschow, C., Schneider, B. & Ueberheide, S. (2014). Tweeting television: Exploring communication activities on Twitter while watching TV. Communications - The European Journal of Communication Re-search (EJCR), 39(2), 129-149. doi:

Carr, D. (2013). Giving viewers what they want. New York Times.

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.

Choudary, S. P. (2013). Why Business Models Fail. Pipes vs. Platforms.

Couldry, N., Fotopoulou, A., & Dickens, L. (2016). Real social analytics: A contribution towards a phe-nomenology of a digital world. The British Journal of Sociology, 67(1), 118?37.

Couldry, N., & Turow, J. (2014). Advertising, big data, and the clearance of the public realm: Marketers’ new approaches to the content subsidy. International Journal of Communication, 8, 1710-1726.

Couldry, N., & Turow, J. (2014). Advertising, big data, and the clearance of the public realm: Marketers’ new approaches to the content subsidy. International Journal of Communication, 8, 1710-1726.

Daidj, N. (2011). Media convergence and business ecosystems. Global Media Journal, 11(19), 1-13.

DiZerega, G. (2004). Toward a Hayekian Theory of Commodification and Systemic Contradiction: Citi-zens, Consumers and the Media. The Review of Politics, 66(3), 445-468. doi:

Day, G. S. (2011). Closing the marketing capabilities gap. The Journal of Marketing, 75(4), 183-195.

Downes, L., & Nunes, P. (2014). Big Bang Disruption: Strategy in the Age of Devastating Innovation.

Doyle, G. (2016). Resistance of channels: television distribution in the multiplatform era. Telematics and Informatics, 33(2), 693-702. doi:

EBU Big Data Conference. (2018). Geneva, 28th of February to 1st of March, 2018.

Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data Consumer Analytics and the Transformation of Marketing. Journal of Business Research, 6(2), 897-904. doi:

Evens, T., & Van Damme, K. (2016). Consumers’ willingness to share personal data: Implications for newspapers’ business models. International Journal on Media Management, 18(1), 25-41. doi:

Felt, M. (2016). Social media and the social sciences: How researchers employ Big Data analytics. Big Data & Society, 3(1). doi:

Ferenstein, G. (2016, Jan., 20th). Netflic CEO explains why a «gut» feeling is still better than Big Data.

Fleissner, P. (2006). Commodification, Information, Value and Profit. Poiesis & Praxis, 4(1), 39-53.

Fuchs, C. (2012). Dallas Smythe Today - The Audience Commodity, the Digital Labour Debate, Marxist Political Economy and Critical Theory. Prolegomena to a Digital Labour Theory of Value. tripleC: Communication, Capitalism & Critique, 10(2), 692-740.

Fortune (2016). How Netflix Is Using Your Data. (Sept 19, 2016).

Freelon, D. (2014). On the interpretation of digital trace data in communication and social computing research. Journal of Broadcasting & Electronic Media, 58(1), 59-75. doi:

Gandhi, B., Martinez-Smith, A., & Kuhlman, D. (2015). TV insights: Applications of big data to television.

Gfk. (2015). Big Questions, Big Answers. Will harnessing smart data for audience analytics save the broadcast industry?

Giglietto, F., & Selva, D. (2014). Second screen and participation: A content analysis on a full season dataset of tweets. Journal of Communication, 64, 260-277. doi:

Gillespie, T. (2014). The relevance of algorithms. In T. Gillespie, P. Boczkowski, & K. A. Foot (eds.), Media technologies. Essays on communication, materiality, and society (pp. 167-193). Cambridge, MA: MIT Press.

Gray, J. (2010). Show sold separately: Promos, spoilers and other media paratexts. New York, NY: New York University Press.

Green, A. (2016). Audience Measurement in the Data Age. IPSOS Connect.

Guardian. (2014). Television must mine bigger data or risk being netflixed.

Hasebrink, U., & Domeyer, H. (2012). Media repertoires as patterns of behavior and as meaningful prac-tices: A multimethod approach to media use in converging media environments. Participations. Jour-nal of Audience and Reception Studies, 9(2), 757-779.

Havens, T. (2014). Media programming in an era of big data. Media Industries Journal, 1(2).

Hepp, A. (2012). Mediatization and the ‘Moulding Force’ of the media. Communications, 37(1), 1-28. doi:

Hepp, A., & Krotz, F. (2014). Mediatized worlds: Understanding everyday mediatization. In A. Hepp, & F. Krotz (eds.), Mediatized worlds: Culture and society in a media age (pp. 1-15). London: Palgrave.

Hermida, A., Fletcher, F., Korell, D., & Logan, D. (2012). Share, Like, Recommend. Decoding the Social Media News Consumer. Journalism Studies, 13(5-6), 815-824. doi:

Hill, S. (2014). TV audience measurement with big data. Big Data, 2(2), 76-86.

Jacobi, C., van Atteveldt, W. & Welbers, K. (2016). Quantitative analysis of large amounts of journalistic texts using topic modelling, Digital Journalism, 4(1), 89-106. doi:

Jenkins, H. (2008). Convergence culture: Where old and new media collide. New York, NY: New York University Press.

Jennes, I., Piersen, J., & Van den Broek, W. (2014). User Empowerment and Audience Commodification in a Commercial Television Context. The Journal of Media Innovations, 1(1), 71-87.

Kackman, M., Binfield, M., Payne, M. T., Perlman, A., & Sebok, B. (2011). Flow TV: Television in the age of media convergence. New York, NY: Routledge.

Kastrenakes, J. (2015, Sep., 23th). Netflix knows the exact episode of a TV show that gets you hooked.

Kastrenakes, J. (2015, Sep., 23). Netflix knows the exact episode of a TV show that gets you hooked.

Kelly, J. P. (2017). Television by the numbers. The challenges of audience measurement in the age of Big Data. Convergence. doi:

Kim, S. J. (2018). Audience Measurement and Analysis. In A. Albarran, B. Mierzejewska, & . J. Jung (Eds.), The Handbook of Media Management and Economics, 2nd ed. (pp. 379-393). Abingdon, Oxford: Routledge.

Kneale, D. (2016, Jan 21). Big Data Dream. Big data is everywhere-now what to do with it? New tools unlock the secrets of consumer desire.

Kompare, D. (2011). More «moments of television»: Online cult television authorship. In M. Kackman, M. Binfield, M. T. Payne, A. Perlman, & B. Sebok (Eds.), Flow TV: Television in the age of media conver-gence (pp. 95-113). New York, NY: Routledge.

Kosterich, A., & Napoli, P. M. (2015). Reconfiguring the audience commodity: The institutionalization of social TV analytics as market information regime. Television & New Media, 17(3), 254-271. doi:

Krotz, F. (2009). Mediatization: A concept with which to grasp media and societal change. In K. Lundby (Ed.), Mediatization: Concept, changes, consequences (pp. 19-38). New York, NY: Peter Lang.

Lippell, H. (2016). Big Data in the Media and Entertainment Sectors. In J. M. Cavanillas, E. Curry, & W. Wahlster (Eds.), New Horizons for a Data-Driven Economy. A Roadmap for Usage and Exploitation of Big Data in Europe. doi:

Livingstone, S. (2015). Active audiences? the debate progresses but it is far from resolved. Communication Theory, 25(4), 439-446.

Lomborg, S., & Mortensen, M. (2017). Users across media. An introduction. Convergence, 23(4), 343-351.

Lotz, A. (2007). The television will be revolutionized. New York, NY: New York University Press.
Mackenzie, D., & Wajcman, J. (1985). The Social Shaping of Technology: How the Refrigerator got its hum. Milton Keynes, Open University Press.

Madrigal, A. C. (2014). How Netflix reverse-engineered Hollywood. The Atlantic.

Mahrt, M., & Scharkow, M. (2013). The value of big data in digital media research. Journal of Broadcasting & Electronic Media, 57(1), 20-33. doi:

Manovich, L. (2012). Trending: The promises and the challenges of big social data. In M. K. Gold (Ed.), Debates in the Digital Humanities (pp. 460-75). Minneapolis: University of Minnesota Press.

Manyika, J., Chui, M, Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.

Mathieu, D., Vicente-Mariño, M., José Brites, M., Amaral, I., Chimirri, N. A., Finger, J., Romic, B., Saa-riketo, M., Tammi, R., Torres da Silva, M., & Pacheco, L. (2016). Methodological challenges in the transition towards online audience research. Participations: Journal of Audience & Reception Studies, 13 (1), 289-320.

McGrath, R. G. (2013). Broadcast TV needs a new business model.

McKinsey Global Institute. (2016). The age of analytics: Competing in a data-driven world (by N. Henke, J. Bughin, M. Chui, J. Manyika, T. Saleh, B. Wiseman, & G. Sethupathy).

Meehan, E. R. (1984). Ratings and the institutional approach: A third answer to the commodity ques-tion. Critical Studies in Mass Communication, 1(2), 216-225. doi:

Mittell, J. (2011). TiVoing childhood: Time-shifting a generation’s concept of television. In M. Kack-man, M. Binfield, M. T., Payne, A. Perlman, & B. Sebok (Eds.), Flow TV: Television in the age of media convergence (pp. 46-54). New York, NY: Routledge.

Murschetz, P. C. (2016). Connected television: Media convergence, industry structure and corporate strat-egies. In E. L. Cohen (Ed.), Communication Yearbook 40 (pp. 69-93). New York, NY: Routledge.

Napoli, P. M. (2011). Audience evolution: New technologies and the transformation of media audiences. New York, NY: Columbia University Press.

Napoli, P. M. (2014). Automated media: An institutional theory perspective on algorithmic media pro-duction and consumption. Communication Theory, 24(3), 340-360. doi:

Napoli, P. M. (2016a). Special Issue Introduction. Bid data and media management. International Journal of Media Management, 18(1), 1-7.

Napoli, P. M. (2016b). The audience as product, consumer, and producer in the contemporary media marketplace. In G. F. Lowe, & C. Brown (Eds.), Managing Media Firms and Industries: What’s So Spe-cial About Media Management? (pp. 261-275). Berlin: Springer International Publishing.

Nelson, J. L., & Webster, J. G. (2016). Audience currencies in the age of big data. International Journal on Media Management, 18(1), 9-24. doi:

O’Ferrell, P. (2015). Big data will impact the television industry?

Parks, M. R. (2014). Big data in communication research: Its contents and discontents. Journal of Commu-nication, 64, 355-360. doi:

Perez, C. (2010). Technological revolutions and techno-economic paradigms. Cambridge Journal of Econom-ics, 34(1), 185-202. doi:

Rogers, M. C., Epstein, M. & Reeves, J. L. (2002). The Sopranos as HBO brand equity: The art of com-merce in the age of digital reproduction. In D. Lavery (Ed.), This thing of ours: Investigating the Sopranos (pp. 42-57). New York, NY: Columbia University Press.

Schäfer, M. T., & van Es, K. (2017). The Datafied Society. Studying Culture through Data. Amsterdam: Am-sterdam University Press.

Scharkow, M. (2013). Thematic content analysis using supervised machine learning: An empirical evalua-tion using German online news. Quality & Quantity, 47(2), 761-773. doi:

Schlütz, D. (2016). Contemporary quality TV: The entertainment experience of complex serial narratives. In E. L. Cohen (Ed.), Communication Yearbook 40 (pp. 95-124). New York, NY: Routledge. doi:

Smith, M. D., & Telang, Rahul (2016). Streaming, Sharing, Stealing. Big Data and the Future of Entertainment. Cambridge, MA: MIT Press.

Smythe, D. W. (1977). Communications: Blindspot of Western Marxism. Canadian Journal of Political and Social Theory, 1(3), 1-27.

Stone, M. L. (2014). Big data for media. Oxford, UK: Reuters Institute for the Study of Journalism.

Trottier, D. (2014). Big Data ambivalence: Visions and risks in practice. In M. Hand, & S. Hillyard (Ed.), Big Data? Qualitative approaches to digital research (pp. 51-72). Bingley/UK: Emerald Group Publishing. doi:

van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197-208.

van Es, K. (2017). An Impending Crisis of Imagination Data-Driven Personalization in Public Service Broadcasters. Ed. by B. Cammaerts, N. Anstead & R. Stupart. Media@LSE Working Paper Series.

Vidgen, R. (2014). Creating business value from Big Data and business analytics: organizational, managerial and human resource implications.

Wagner-Pacifici, R., Mohr, J. W., & Breiger, R. L. (2015). Ontologies, methodologies, and new uses of Big Data in the social and cultural sciences. Big Data & Society, 2(2). doi:

Williams, R. (2003[1974]). Television: Technology and cultural form. London, UK: Routledge.

Wirth, W., Von Pape, T., & Karnowski, V. (2008). An integrative model of mobile phone appropriation. Journal of Computer-Mediated Communication, 13(3), 593-617. doi:

Wywy. (2016). Programmatic TV: How it works, the players & the right strategies.
Murschetz, P. C., & Schlütz, D. (2018). Big Data y televisión. Una reflexión crítica sobre el auge del Big Data como nuevo paradigma tecno-económico, y su impacto en el concepto de target de audiencia. Fonseca, Journal of Communication, (17), 23–38.


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