New Media- Big Data. Amy McCarthy Week 10
It is not hard to see that new media is a powerful tool. It is a part of our lives now and fits in seamlessly, none more so than the internet. I myself spend most of my day somewhat connected to the internet, whether if be on Facebook, Instagram or checking emails. But it's what goes on behind the scenes that is interesting- everything I do can turn in to data that will benefit someone. What is more important though, is what is done with all that data. Coming to conclusions and making predictions is what can make a difference. Predictive data can affect your day in every way possible, from the way you drive, shop, study, vote, watch TV and communicate (Siegel, 2013). A lot of this is to do with marketing, and how consumers are targeting differently according to predictions. Marketing is benefitting hugely from collecting online data. Companies like Netflix show recommendations by making use of data collected from consumers. This way they can suggest what a consumer should purchase next (Shaw, 2014). This form of target marketing is extremely useful and can essentially turn a much larger profit. Similarly companies predict 'mouse clicks' of online advertising. Websites generally pay per click for the advertisements they display, therefore they predict which ad you're more likely to click on (Siegel, 2013). Whenever I'm on Facebook, Google or YouTube the only advertisements that seem to show up are for companies I am actually interested in. It's like they know me really well. You would think with all of this knowledge that companies would take advantage of it. But from my experience this isn't really the case. Companies like General Pants Co, Universal etc have all implemented 'data bases' that send you promotional emails. The idea of this is that every time you make a purchase online or in-store the data base would track your purchases- learn your favourite brands or styles then send you emails accordingly. Whether or not this has actually worked is unsure, but if the program was implemented correctly it could be a huge profit generator. On the other hand, insurance companies have the capabilities to predict if someone is likely to crash a car or injure themselves (Siegel, 2013). An insurance company called Allstate predicts bodily injury liability from car crashes based on the characteristics of the insured car which saves approximately $40 million per year. Obviously there are limits to the accuracy of these predictions, Jay Leno once asked "How come you never see a headline like 'Psychic Wins Lottery'?" (Siegel, 2013). Which is right- "prediction is very difficult, especially if it's about the future" (Bohr, unknown) because the future IS unknown (Siegel, 2013). REFERENCE LIST: -Shaw, J. (2014, March-April). Why “Big Data ” is a Big Deal. Harvard Magazine. Retrieved from http://harvardmagazine.com/2014/03/why-big-data-is-a-big-deal -Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc. -The Economist. (2012, June 26). What is Big Data? [Video file]. Retrieved from https://www.youtube.com/watch?v=ahZGEusG13A










