Big Data & Telemetrics
Finally, we can talk about one of my favourite things to do to relax. Watch TV!
Okay, so I’m sure that there are a great many people out there who would say that the fact that there are huge companies collecting our data and analyzing it and doing who knows what else to it, let’s be honest.. If it makes for a better TV show are how much do we actually care?
I guess, it’s not really as big a deal for me personally. You see I’m not on twitter and I won’t distract myself by going into detail on that I actually just don’t see the appeal but on the other hand, those times where I do get sucked into watching what ever reality TV show is showing for the season (Okay, I actually love The Voice and can’t wait to watch the blind auditions) I do enjoy reading those witty remarks all those twitter uses share with us! Good work, love your stuff!
But what does this have to do with the title, “Big Data”? Well think about every piece of information you have produced; facebook post, movies recommendations, purchase history, emails, tweets and so on. It all get’s collected as one big piece of data which scarily enough grows “by an estimated 2.5 quintillion bytes per day”. (Siegel 2013) (If you’re not up to scratch on your quintillion, it’s a lot!) So basically, all of this data that is being accumulated by a great number of machines “unleashes the power of this exploding resource.” (Siegel 2013) By use of this data companies, analysts or whoever it is sifting through this information is able to learn a great many things about what drives people to make a decision or take a certain action.
So let’s take this back to TV. For the purposes of trying to help myself get around all these concepts, let me attempt to use myself as part of an example. In a research paper produced by Woodforrd, Prowd and Brus looking at social media engagement with television they have some very interesting figures and finds to show. Let’s try using an example!
You see I LOVE the TV show Once Upon A Time! If you haven’t watched it, I will admit it’s a little slow to get into but once you’re hooked, it’s the best! And I continually feel blown away and a little envious of the amazing talent and creativity of the writers of the show. While unfortunately, we don’t have access to American Television, fortunately there are a great many sites I am able to access to watch my favourite shows, which I never use! Anyway, one of the things I have found with a couple of shows I keep up with that are aired on the American ABC channel is that they pretty much air each season in two parts. I’ve always found this really frustrating and to be honest have never given a great thought as to why this might occur. Important American events maybe, who knows?
Well, I’ve finally figured it out! Based on this document I referred to before they have found that when a TV series is split it to two parts rather then running continuously over the course of however many weeks there is a variance in the number of viewers. When there is a break, they are able to draw back viewers they have potentially lost with their “mid-season return” and have more viewers over the course of a season when it’s show split due to time in which they can advertise and build back up excitement within viewers.
How clever for them but still annoying for me!
Still, I shouldn’t complain. You see, you know all those twitter users I spoke about before. Well, because of this information collection that keeps on occurring, over the past few years TV show writers have been able to connect more with audiences and tailor their TV shows more to how viewers are responding. So that really annoying character in that show you love, depending on response looks like they could stay or go! Anything could happen!
To conclude, I do feel very TV is concerned it is a kind of cool to be able to use data in this way but I shouldn’t be too carefree as always when it comes to collecting information about people, there can be a very fine line not to be crossed!
References
Harrington, Stephen. 2013. “Ch 18 Tweeting about the Telly: Live TV, Audiences, and Social Media.” In Twitter and Society edited by Katrin Weller, Axel Bruns, Jean Burgess, Merja Mahrt & Cornelius Puschmann, 237-248. New York, NY: Peter Lang
Siegel, Eric. 2013. “Introduction – The Prediction Effect.” In Predictive Analytics, 1-16. Hoboken, NJ: John Wiley and Sons Inc.
Woodford, Darryl, Katie Prowd and Axel Bruns. (forthcoming). “Telemetrics: Towards Measuring Social Media Engagement with Television.”
Images
http://www.pinterest.com/pin/83527768060945481/ Retrieved 13th May 2014
https://www.google.com.au/search?q=twitter+memes&es_sm=119&tbm=isch&tbo=u&source=univ&sa=X&ei=E6x4U49Pyu6SBe2CgagE&ved=0CCkQsAQ&biw=1172&bih=568#facrc=_&imgdii=_&imgrc=YxdP4ddRVVev8M%253A%3BhHb87EUeZKRYIM%3Bhttp%253A%252F%252Fmlbfancave.mlb.com%252Fassets%252Fimages%252Fcustom%252FChipper_meme_4_i52ytr7o.png%3Bhttp%253A%252F%252Fmlbfancave.mlb.com%252Ffancave%252Fblog%252Farticle.jsp%253Fcontent%253Darticle%2526content_id%253D35953864%3B539%3B363 Retrieved 13th May 2014
http://www.pinterest.com/pin/129900770476707803/ Retrieved 13th May 2014












