The quality of our quantified self

When I initially began thinking about the thematic focus for this subject ‘Media Lives’, I began questioning the meaning of those two words. Does the media define lives? Or does media ‘live’; as in to say it is alive?

With this focus, I want to launch into the notion of quantifying oneself. Nowadays, we can (and many do) record analytical data about pretty much anything. What we eat, how we eat, how we sleep, how much we’ve moved, our purchasing patterns, our sociality, our location and the list goes on… This concept shouldn’t be new to me, or anyone in my generation, I’m sure. Anyone who snagged a digital pedometer out of the Fruit Loops box circa 2005 would understand.

baby+lucent+system+overview

See?! Even babies are doing in!

Exploring the phenomena of the ‘selfie’ provided an initial understanding of self-quantification. Measurable ‘meaning’ is brought to ‘selfies’ and the owner through ‘status affordances’ such as likes, followers, views, plays or subscribers; metrics that provide comparable levels of importance depending on the individual. Both the individual and others make inferences according to these metrics and it has the potential to be both extremely beneficial and conversely detrimental, depending on the context of the individual. Tiindenburg and Gomez Cruz reveal that visual self-presentation online is seen by some as leading to increasing control, agency and power (Koskela, 2004; Walker, 2005) Taking photos of oneself is not a particularly new concept (Joseph Byron c. 1910) and probably stems from a more basic curiosity, found in not just our species. Homo-sapiens are curious creatures, there is no denying that. The fascinating element of ‘selfies’ is their context on social media, and the way intangible acknowledgments are used as a currency of social importance.

Thanks to tech advancement, life-logging is a very familiar, much more recent concept emerging across the developed world. From a device as small as a coin, we are able to collect information about our behaviours, physiology, biochemistry, emotional response, to name a few, all curated to individual preferences and spat out as nothing more than numbers and measurements. From this, WE the individuals draw meaning from this data, transforming it into something a little less ambiguous and uncontrollable. Such data can be useful. Very useful. Not only to the individual for a plethora of reasons such as improving work-life balance and monitoring health; but to other ‘identities’ as well. When combined with millions of other personal data collections spanning years and years, this data becomes BIG and it’s usefulness for revealing emergent patterns in human behaviour and dynamics grows with it. As Nicky reiterated in Week 2 lecture, ‘we use other people as a mirror in which we see ourselves’.

But what about big data? Such a mammoth amount of information requires tangible, equally mammoth in scale, storage. As patterns reveal ever rapidly increasing amount of data creation, volume, variety and velocity are the “key data management issues” (Sicular, 2013) with industry analyst Doug Laney supporting these claims. Concerns about privacy and security surround big data management and how this data might potentially be used by ‘other’ industries and potentially commodified, restricting the benefit for collective awareness.

Sean Gourely’s TEDtalk raised some fascinating points about big data and collaboration. Perhaps the most salient point emerged following the story of a group of ‘grandmaster’ chess players struggling to compete with the logistical efficiency of the computer that was programmed to play. What was discovered from this what that the man+machine team were incomparably better than the machine or grandmaster only teams. Gourely introduces ‘Augmented Intelligence’, which can be found in the middle of the spectrum from the mathematical processes we use to simplify our complex world and the visualisation humans use to enhance our cognitive ability. Gourely’s insights could be useful for understanding why big data must be managed using a ‘humanised’ approach, for the greater social good. Such technology is only beneficial when paired with proper guidance. Theoretically, big data is should facilitate shrinking degrees of separation, yet is it not bypassing a large majority of the globe who simply cannot access such media? And could this further exacerbate social, political and economic disparities?

Wolf’s proposition of ‘turning inward’ and not viewing such measurements for their outwardly good is exactly the focus we should be applying to such an issue. Wolf explains that any benefit should come from individuals having a greater understanding of their own behaviours and therein be more perceptive to the world around them and patterns of social behaviour. Self-quantification is not only an entity external to us but is also an extension, a rather integral aspect of our being. It would seem there is a logical connection here, the bigger the data gets, the smaller the world comparatively seems.

References:

Evans, N 2016, ‘Looking At Ourselves’, lecture, 12 March 2016, UOW, viewed 9 March 2016

Marwick, A. E.. 2013) “Leaders and Followers: Status in the Tech Scene”. Status Update: Celebrity, Publicity, and Branding in the Social Media Age Pg 73-111 Yale University Press. Accessed from http://www.jstor.org/stable/j.ctt5vkzxr

Tiinderberg, K. Gomez Cruz, E. 2015. “Selfies, Image and the Re-making of the Body” Body & Society. Vol.21 pg 77-102. Sage Publisher. Accessed from https://moodle.uowplatform.edu.au/pluginfile.php/607709/mod_resource/content/1/SelfiesImageandtheBody.pdf

Wolf, G 2010, ‘Gary Wolf: The Quantified Self’, TEDx online video, viewed on 10th March 2016, accessed http://www.ted.com/talks/gary_wolf_the_quantified_self?language=en

Sicular, S. 2013 ‘Gartner’s Big Data Definition Consists of Three Parts, Not to Be Confused with Three “V”s’, Forbes Tech site, article, accessed http://www.forbes.com/sites/gartnergroup/2013/03/27/gartners-big-data-definition-consists-of-three-parts-not-to-be-confused-with-three-vs/#6ef1ee663bf6

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