New Media, Big Data and Telemetrics:
This week we were introduced to the notion of “big data”, a concept that classifies our online participation as a new kind of knowledge just waiting to be organized and interpreted. In this weeks lecture Darryl Woodford discussed new media and telemetrics, defining big data as “a currency across industry, business intelligence and data markets” (Woodford, 2014). Societies prolific use of social media has prompted television and media bodies to utilize this medium as a tool for measuring and interacting with audiences (Ibid, 2014).
 “Big data is used to describe the exponential growth and availability of data, both structured and unstructured” (SAS, 2014). As early as 2001 analyst Doug Lanely articulated the potential big data poses to those organizations that accurately analyse it. Sites like Twitter and facebook hold “data that embodies a priceless collection of experience from which we can learn” (Siegel, 2010). That’s right, no matter how benign or mundane the tweeter post, that public declaration on your newsfeed is encoded as data and warehoused (Siegel, 2010). As discussed, Twitter is at the forefront of big data analysis, and as Axel Bruns discussed, “Twitter as a platform, and can be understood as corresponding to micro, meso, and macro layers of information exchange and user interaction”.
  Twitter trends are increasingly conceptualised through user “hash-tagging”. Every time a user “hash-tags” they are essentially marking a tweet as being relevant to that specific topic, effectively making it more discoverable to other users and researchers (Bruns, 2013).
  Woodford’s article, “Telemetrics: Towards Measuring Social Media Engagement with Television”, provided an insight into the development of sophisticated and nuanced metrics to understand how Twitter uses are reacting to the content of particular broadcasts (Woodford, 2014). The article discussed two modes of evaluating the performance of television shows through twitter. The twitter excitement index, performs show-to-show comparisons of audience engagement as it measures the volatility of Twitter discussion throughout the show. This method thus allows comparisons across genres and between networks and countries (Woodford, 2014). The second form of twitter analysis is undertaken through a seaonsonal model evaluating episodes independent of their context (ibid, 2014).
Google’s’ recent investment of $130 million to finance the start of Flatiron Health Inc. represents the financial worth big data possess. (Hay, 2014) Flatiron health aggregates cancer-patient data from a wide variety of sources, and with Google’s investment it can now allow doctors to acquire patient records to help make more informed treatment decisions. (Ibid, 2014).Â
  We live in a hyper-personalized world, and the analysis of big data by organisations permits them to personalize content dependant on the needs of the consumer. The leveraging of big data tools by organisations is just another instance in new medias continual evolution. As this process benefits both the consumer and the organisation I can only advocate its use.Â
   Woodford, Daryl. “KCB206 Internet, Self and Beyond: Week 9 lecture notes.” Accessed May 7, 2014. http://www.dpwoodford.net/wp-content/uploads/2014/02/KCB206-Big-Data-Lecture_Small.pdf
 Siegel, Eric. 2013. “Introduction: The Prediction Effect.” In Predictive analytics: the power to predict who will click, buy, lie or die, 1-16.Hoboken, NJ: Wiley
  Woodford, Darryl, Katie Prowd and Axel Bruns. (forthcoming). 
   “Telemetrics: Towards Measuring Social Media Engagement with 
   Television”. Accessed May 8, 2014.
   http://blackboard.qut.edu.au/bbcswebdav/pid-5234702-dt-content-
   rid-2118244_1/courses/KCB206_14se1/Woodford%2C%20Prowd
   %20and%20Bruns%20-%20Telemetrics%20Towards%20Measu
   ring%20Social%20Media%20Engagement%20with%20Television
   .pdf
Bruns, Axel. 2013. “Follower Accession: How Australian Politicians Gained their Twitter Followers.” Mapping Online Publics Blog, July 8. Accessed May 9, 2014. http://mappingonlinepublics.net/2013/07/08/follower-accession-how-australian-politicians-gained-their-twitter-followers/
  Hay, Timothy, 2014 “Google Ventures Leads $130M Round For Big Data Medical Software Company Flatiron Health” Wall Street journal, may 7th, Accessed may 9th, 2014. http://blogs.wsj.com/venturecapital/2014/05/07/google-ventures-leads-130m-round-for-big-data-medical-software-company-flatiron-health/
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