Week 10: Big Data and Telemetrics
This week’s readings discuss the ideas and purposes of big data and telemetrics which are ways academics and corporations gather, process, analyse and visualise data. The information we as internet users put into the public domain is part of this process; we human beings ARE data. Big data is used across industries for various purposes such as market research, election and stock market predicting, and predicting behaviours and analysing them. Telemetrics involves the study of the relationship between social media use and the performance of television shows. Firstly this blog will look at the predictive analytics of big data and its corporate value, as discussed in the reading, Predictive analytics: the power to predict who will click, buy, lie or die. Siegel defines predictive analytics as “technology that learns from experience (data) to predict the future behaviour of individuals in order to drive better decisions” (2013, 11). This data is us – the personal information that we share, generally online, that is then collated by corporations to target the right people for the right products and services. Each “incident, event and transaction” that we are involved in, becomes data that is stored; an amount that is growing by around 2.5 quintillion bytes everyday (Siegel 2013, 3). The ability to predict how a consumer is going to act, not specifically but vaguely, is an incredibly powerful tool for organisations to harness. This “machine learning” is a process that “has its roots in statistics and computer science” (Siegel 2013, 4). However, prediction isn’t about knowing for certain which people will act in a certain way, but knowing which people generally behave in particular situations. After all, as Siegel says, “A hazy view of what’s to come outperforms complete darkness by a landslide” (2013, 11). Telemetrics is studying social media, particularly Twitter, and the relation to TV performance. “Social media is increasingly playing an important role in television; playing a key role in the promotion of upcoming shows, being utilised for on-air interactions with the audience” as well as being a gauge in determining the success of particular programmes (Woodfood, Prowd and Bruns, n.d). In Telemetrics: Towards Measuring Social Media Engagement with Television, the authors discuss what they call the “Twitter Excitement Index” which they use to “understand how Twitter users are reacting to the content of particular broadcasts” (Woodfood, Prowd and Bruns, n.d). This is more accurate than traditional ratings, as through Twitter researchers are able to see what audiences think of certain shows, as opposed to ratings that can only show the size of the audience. Using a Seasonal Model, Woodford, Prowd and Bruns have examined the trends in television show performance, noticing that a show’s pilot rates higher than average then drops about mid-season, before continuing to build up towards the final episode. Although analysing Twitter seems to be more of an accurate depiction of a show’s performance throughout a season, as opposed to the traditional ratings system, it is flawed in that it only represents those shows that have an audience segment actively using social media. In conclusion, I am beginning to see how this all relates back to my discipline (film and TV), as predictive analysis is relevant in the film industry, such as predicting which demographic is more likely to watch particular genre films and how to market them accordingly. Also, telemetrics are very useful in the TV industry as what audiences say on social media affects a show’s performance and measures its success with certain audiences. REFERENCES Siegel, Eric. 2013. Predictive analytics: the power to predict who will click, buy, lie or die. Hoboken: Wiley. Woodford, Darryl, Katie Prowd and Axel Bruns. (forthcoming). “Telemetrics: Towards Measuring Social Media Engagement with Television.”









