How big data is used to predict music's next "big thing". Christine E. Hansen
As pointed out in my previous posts, new media have enabled a vast number of new opportunities to connect, share and communicate – in addition; it has also enabled organisations to collect data. Through our participation in several new media channels, companies more often collect data about consumer behaviour. Siegel (2013, 3) explains that “data embodies a priceless collection of experience of which to learn”. If we allow organisations to collect data about our behaviour, it will, according to Siegel (2013), enable them to make predictions that may benefit the organisation.
When we share content on Facebook or Twitter, stream music, watch videos on YouTube, purchase products on amazon or search Google for our next holiday; online organisations store this information, data, and use it to “uncover what drives people and the actions they take, what makes us tick and how the world works” (Siegel 2013, 4).
Kadhim Shubber (2014) reports in The Guardian that social media platforms and streaming sites have enabled record labels and other music industry stakeholders to acquire access to immense quantities of data on listening habits and contemporary trends.
Not long ago, our listening habits were relatively private, and we could listen to our favourite music without Spotify or Last.fm storing the information or sharing it with the rest of the world. Before organisations gathered data; before new media, record companies had an overview of where their CD’s were popular, and which radio channel played their songs, however, that information only painted an incomplete picture (Shubber, 2014). Yet, Shubber (2014) states that the new explosion of data from several sources such as torrenting, music streaming sites and social media platforms has given the music industry a great opportunity to appreciate their fans and find upcoming artists, like never before. Simultaneously, as the Internet is stripping the power away from record labels, it is also empowering them to foretell future hits.
Likewise, other branches of the music industry such as the mobile app “Shazam”, which enables users to recognise music and TV around them, is using stored data to predict artists that will most likely receive a growing attention in the nearest future (Datoo, 2013). The app combines critics’ evaluations together with the number of people that have utilised Shazam to search for a song, in order to comprehend which artists that are currently creating a high level of interest. By doing this, Shazam is able to make use of consumer behaviour to better evaluate the artists that have already begun to raise interests and that are on the way to attain traction.
With the new knowledge gained by data collection, Siegel (2013) contends that prediction is possible. Furthermore, by connecting the latter example of utilising data to the aforementioned statements by Siegel, the data collection is most likely benefiting organisations in several ways, but primarily, it allows them to predict new trends and how consumers may behave in the future. Conversely, one could beg the question if the collection of data is as benefiting to the consumers, or if it belongs to the discussion regarding exploitation of users.
References
Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc.
Datoo, Siraj. 2013. "How Shazam uses big data to predict music's next big artists". The Guardian, December 10. Accessed May 9, 2014. http://www.theguardian.com/technology/datablog/2013/dec/10/shazam-big-data-prediction-breakthrough-music-artists#start-of-comments
Shubber, Khadim. 2014. "Music analytics is helping the music industry see into the future". The Guardian, April 9. Accessed May 9, 2014. http://www.theguardian.com/technology/2014/apr/09/music-analytics-is-helping-the-music-industry-see-into-the-future