Where does big data sit in the digital ecosystem?
Man generated 5 exabytes of data up until 2003 (Intel 2014). In 2012, data grew 500x more data than all data ever generated: predicted to grow 3 times bigger by 2015 (Intel 2014). The internet has facilitated measurable and organised approaches to harvesting this data. Big data (as it’s called) is bought, sold and traded as the currency of modern business (D Woodford 2014). As such, we must readdress notions of fact and fiction, as more businesses creep into our daily lives. How big data is utilized to generate knowledge? What industries are capitalizing over others?
If you know what to look for, you can make the most of big data (G Bell 2014). In this respect, data is in the eye of the beholder, not the machine that processes it. Part of this process means understanding the core components of facts, analytics, visualisation and algorithms (D Woodford 2014). Facts are complicated, and always partial from the start (G Bell 2014). In fact, when I signed up to Myspace, I was technically too young to use the site. My friends and I claimed to be obscure ages such as 105, which was the case with 15% of users (J Bullas 2014).Similarly, power distances become important in how we determine fact from fiction. The context and relationship with another (person, organisation etc.) encourages us to think differently about the data we provide.For example,I'm probably going to be honest when talking about new work policy opinions to other staff, probably not when talking to the manager.According to Mark Vincent (from Shelston intellectual property) Data comes with responsibilities and challenges, which calls to mind the position of the Government on issues such as privacy and misleading information (2014).
Visualization provides new frameworks for presenting facts, while analytics means discovering and communicating meaningful patterns (D Woodford 2014). As facts move along the chain, we start to inject our own opinions, interpretations and ideologies. This means data continues to change, especially in the algorithm phase (G Bell 2014). In the digital age, in such a cluttered environment, which facts are the most relevant to study? Social media is a clever tool for tracking customer buying, viewing and consumption habits to determine a brands impact. Hashtags can work as markers of a topic, issue or event, allowing data to be organised in online communities (Bruns & Moe 2013). The point is here, that algorithms are about making comfortable, preferable truths that we can handle and resonate with. When you type a search into google, it automatically populates the rest of the answer (Siegel 2013) just as netflicks recommends movies based on your personal data (G Bell 2014).
(screenshot: google, 2014)
Humans hate change and the feeling of cognitive dissonance. These recommendation algorithms are successful because they capitalize on the familiar (Intel 2014). The content television ecosystem is well aware of this. As such, there are more housewives than ever, with reality TV booming. Game of thrones producers were clever, they saw opportunity for narrative based drama, and it became the most popular show released in 2013 (IMBd 2014).
(game of thrones, 2014)
Only 10-15 percent of organisations fully capitalize on big data (Gartner group 2012), and will outperform competitors by 20% (Gartner group 2012). So, there is potential to gain insight and competitive edge from big data. Innovative approaches are required to sift through mountains of data to uncover the golden nugget of knowledge and insight. The challenge is to use algorithms so they deliver familiar and novel, exciting experiences. Ultimately, technology doesn’t make facts; it can’t control emotions, as technological determinism would beg to differ. Humans are the ones in control, and we are only limited by our own intellects and imaginations.
Bell, Genevieve. 2014. “The secret life of big data” Inside HPC accessed 07/05/14 http://insidehpc.com/2014/02/23/video-intels-genevieve-bell-keynotes-sc13/
Bullas, J 2014. “22 social media facts and statistics you should know” Jeffbullas.com Accessed 29/04/14 http://www.jeffbullas.com/2014/01/17/20-social-media-facts-and-statistics-you-should-know-in-2014/
Gartner Group, 2012. “The importance of big data: a definition” Gartner Accessed 02/05/14 https://www.gartner.com/doc/2057415/importance-big-data-definition
IMBd, 2014. “Most popular TV series” IMBd accessed 29/04/14 http://www.imdb.com/search/title?title_type=tv_series
Intel, 2014. “How Big Data works” Intel IT centre accessed 07/05/14 http://www.intel.com/content/www/us/en/big-data/unstructured-data-analytics-paper.html
Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc.
Vincent, M. 2014. “Australia: the application of traditional legal rights in a big data world” Mondaq Accessed 07/05/14 http://www.mondaq.com/australia/x/290666/Copyright/The+application+of+traditional+legal+rights+in+a+big+data+world
Woodford, D, Prowd, K & Bruns, Axel. “Telemetrics: Towards Measuring Social Media Engagement with Television.” Accessed April 24, 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%20Measuring%20Social%20Media%20Engagement%20with%20Television.pdf















