Quantifiable You: Introducing our data doubles and their value to digital society
Updated: May 30
Think about the space you occupy online. Every Google search you’ve ever made, video you’ve watched on Youtube, post you’ve interacted with on Facebook - if you could see the full catalogue of your online presence, would it be an accurate representation of you?
The data trails you and I leave in our digital wake reveal some of the most intimate parts of who we are. However personal they are to us, they are just as—if not more— valuable to big businesses. How can we begin to conceptualise our relationship with our own data within the context of our digital age? In this article, we’ll break this question down and introduce you to the “quantifiable” you - the version of you that can be broken down into bitesized data points that help keep the networked economy afloat, and the machine learning and re-learning ad infinitum.
Picture this: you’re catching up with a friend over lunch. From the corner of your eye you recognise your neighbour, sat a table down from you, hunched over a paper and pen. When your food arrives, they casually walk over to your table, say hello, and take a glance at the food before going back to their notebook and scribbling away what you ordered. It’s a strange scenario to envision, right? Not if you were living in early 20th century England. The mid-1930s marked the beginnings of what came to be known as the ‘Mass Observation’ (MO) movement in the UK, viewed by many as the predecessor of today’s digital tracking movement (Abend et al., 2016).
In an open letter, the founders of MO called on individuals around England to observe any conceivable aspects of their daily lives - behaviours, attitudes, consumption habits, you name it - and report them back in the form of diary entries. Described by its founders as a new form of “anthropology of ourselves,” MO set out to develop a self-sustaining database that would assist in the systematic, wide-scale study of social behaviours in Britain (Harrison et al., 1937). The goal was to use these insights to inform and shape policy on pressing social issues. By 1949, the movement’s focus had shifted to consumer behaviour, and MO was registered as a limited company (Mass Observation).
If any of this is sounding familiar, it’s because it is. Fast forward to almost a century later, and not much has changed; the physical, nosy figure sat at the table next to yours at lunch has transformed into the metaphysical structure we all know and love - the internet.
MO formed the bedrock for what was to come in the digital era. Much like MO, many of the early movers and shakers of the digital era entered online space with the promise of some form of social service or impact. Take Facebook; the tech conglomerate launched in 2004 as an online social network connecting Harvard students to one another. And again, much like MO, Facebook went public on the stock market less than a decade after its official launch. The parallels between the two are striking. What do both Facebook and MO have in common? Access to a treasure trove of rich, naturally occurring data, and the means to extract value from that data.
We’ve beaten the saying ‘data is the new oil’ to death, but for good reason - both of these examples showcase the immense value our data holds. The term ‘datafication’ was developed to describe this phenomenon; the reconstruction of social actions, interactions, and behaviour into quantifiable data that can be used to amass value - monetary or otherwise (Mayer-Schönberger & Cukier 2013).
Today, rather than scribble our attitudes, opinions, and behaviours into a small notebook, we subscribe to online content that reflects our world views (talk about an echo chamber); we download apps to keep track of our activity and sleep; we opt into e-banking services to help us keep track of our finances; we hand over our DNA to companies that claim to track our heritage, and the list goes on.
What exactly was it that drew the MO diarists to record intimate details of their daily lives and volunteer them? What is it that compels us to do the very same thing today? Without getting too philosophical (I’ll leave that to our resident AI Philosopher), we can answer this question by diving a little deeper into the idea of quantified selves.
Put simply, the quantified self refers to “any individual engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information” (Swan, 2013). Your quantified self is the version of you that is made up of the various measurable components - or data points - that make you, you. Notice the emphasis on self; in theory, our quantified selves are formed through our own volition, through the parts of ourselves we choose to observe and report (though the reality is often less straightforward than that). Although the notion of a quantified self has been around since time immemorial, the digital quantified self is uniquely different to its analogue predecessors.
This movement, which began in 2008, was promoted as a form of “self-knowledge through numbers” (Quantified Self). Unlike previous iterations of the quantified self, this self-knowledge is facilitated through the use of digital technologies in the form of devices and wearables, and through the use of analytic tools (Barry, 2020). The focus of the movement is on developing a form of reflexive self-management through monitoring and feedback technologies that purport to help individuals gain a clearer understanding of their body and mind, and therefore make better choices for themselves.
While its focus has shifted since its inception, the movement’s key tenant remains: technology’s embodiment in our daily lives provides us with a deeper understanding of ourselves. It appeals to the most basic human instinct of self-preservation by providing individuals with the ability to make informed decisions when it comes to their wellbeing. And so, over the past two decades, countless new means of self-tracking have emerged to help us manage nearly every aspect of our lives, and our quantified selves have multiplied exponentially as a result.
Thanks to AI and ML systems, we’re able to make better sense of all the moving data pieces that make up our quantified selves and that of others. Where the collection and analysis of data was once a manual, time-intensive task that required a specific skillset to undertake, our data is now easily “collected and crunched by algorithms” that are able to provide instantaneous, actionable feedback (Barry, 2020). In a nutshell, the data you input is fed back into a system which granulates it into numerical outputs, aggregates it and analyses it against other quantified points, and derives meaningful insights in real time; this is exactly where companies like Synapse Analytics come in.
Let’s stop for a minute and reflect on what we know.
We have a brief understanding of the history of our quantified selves, and how the digital era ushered in a new form of networked citizens. We also know that our data is a valuable resource in helping us make sense of ourselves and our place in the world. But we also know that we aren’t the only beneficiaries of our data. For every meaningful insight you and I are able to draw from our interactions with self-tracking technologies, the architects of the software and hardware are able to capitalise on those insights tenfold.
As of 2019, the digital world encompassed an estimated 44 zettabytes of data (Desjardins, 2019)(1). Your data powers entire business ventures that provide you with key services, it informs governmental policies that impact you directly, it trains AI systems that streamline your daily activities, and so on. In short, your data holds immense value. Where value and profits flow, regulations must follow. Globally, regulatory entities have emerged to oversee the dissemination and use of our personal data. In July 2020, Egypt’s very own Personal Data Protection Law came into effect, adapting aspects of global data regulations to Egypt’s own digital and legal landscape. Like its international counterparts, this national effort aims to regulate the use of personal data in order to ensure privacy and individual autonomy.
But the integrity of these efforts has been questioned and repeatedly undermined. The same businesses that use your data to provide you with services simultaneously use that very data to deny you access to those very same services (banks and credit scores); the policies your data informs can often times be repressive and exclusionary (media censorship and restrictions on internet freedoms); the AI systems that streamline your activities can perpetuate biases and inequality (check out this earlier article on unethical algorithms ). Privacy has taken on new forms in the digital age, the interpretations of which continue to shift as the digital world continues to expand. The ubiquity of digital technology seems to have eroded the lines between our private selves, and our public, quantified selves.
You are a key part of this vast and intricate network of quantified selves. By existing in our networked society, you can consider yourself a data beacon emitting data bites. Your data is a powerful and valuable resource - it wouldn’t be an exaggeration to call it the life blood of the networked economy. So it’s worth considering, who is benefiting from your data? To what ends is your data being used as a means? How much control do you have over your data once you hand it over to the apps and their developers? The next time you hastily accept the terms and conditions, take a minute to reflect on the parts of your(quantified)self you're handing over, and what you're getting in return.
1) To put that into perspective, “if each Terabyte in a Zettabyte were a kilometer, it would be equivalent to 1,300 round trips to the moon and back (768,800 kilometers).” (Cisco).
Abend, P., Fuchs, M., Reichert, R., Richterich, A., & Wenz, K. (2016). Digital Culture & Society: Vol. 2, Issue 1/2016 “Quantified Selves and Statistical Bodies”.
Barry, L. (2020). The quantified self and the digital making of the subject. In M. Stocchetti (Ed.), The digital age and its discontents: Critical reflections in education (pp. 95–110). Helsinki: Helsinki University Press. https://doi.org/10.33134 /HUP-4-5
Desjardins, J. (2019). How much data is generated each day? Retrieved from https://www.weforum.org/agenda/2019/04/how-much-data-is-generated-each-day- cf4bddf29f/
Harrisson, T., Jennings, H., Madge, C. (1937). “Anthropology at Home,” In: New Statesman and Nation, January 30, 1937.
Mass Observation. ‘History of mass observation.’ http: www.massobs.org.uk/about/history-of-mo.
Mayer-Schoenberger, V. and K. Cukier. (2013). Big Data. A Revolution that will transform how we live, work, and think. London: John Murray Publishers.
Quantified Self. https://quantifiedself.com/.
Swan, M. Big Data. Vol. 1, Issue 2/Jun (2013) “The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery” 85-99.http://doi.org/10.1089/big.2012.0002