Can Strava be said to produce ‘objective truth’ from ‘raw data’?
In this commentary I tie main points from the introductory chapter, written by Gitelman and Jackson, of the book “Raw Data is an Oxymoron” (Gitelman), with data produced by the application Strava as an object of self-quantification to critique the perceived objectivity of data-producing practices by referencing critical data scholars regarding the epistemic validity and ontological implications of such.
The role of “raw data”
The main argument, which is also the title of the book, is inferred in the absolute beginning from a quote by Bowker, stating that “’Raw data’ is both an oxymoron and a bad idea.” (Bowker). It targets the understanding and epistemological value of data, seen by many as objective mediators of information, or carriers of ‘truth’, and aims to challenge the mainstream notion of objectivity itself. The book, in an overall view, is dedicated the enquiring into data’s fundamental role in society and knowledge-production, which is undergirded by referencing Manovich in that data do not and cannot simply exist on its own, but have to be generated and imagined as data in order to exist. (Gitelman 3; Manovich).
Gitelman & Jackson further refer to one of the main points from the first chapter of the book, written by Rosenberg, stating that “’the semantic function of data is specifically rhetorical.’ Data by definition are ‘that which is given prior to argument,’ given in order to provide a rhetorical basis” (Gitelman and Jackson 7), to reach a powerful argument of their own; exactly because data can stand as a given it’s possible for them to be taken to establish a model adequately unto itself; “given certain data, certain conclusions may be proven or argued to follow. Given other data, one would come to different arguments and conclusions.” (Gitelman and Jackson 7).
Strava as an object of enquiry into self-quantification
The term Quantified Self originated with the two Wired Magazine editors Wolf and Kelly in 2007 (Lupton 12–13; Wolf). Becoming widely commercially available with the release of the Apple Watch in 2015, an era of self-tracking technologies was ushered in with increased pervasiveness within everyday and mundane activities (Pink et al.), thus mediating participants’ ways of perceiving and experiencing being in the world (Pink and Fors 376) through, assumed, objective data- and knowledge-producing practices.
Strava is briefly highlighted by Lupton (23–24) as a digital self-tracking platform incorporating gamification elements in order to motivate the continued use and sharing on the platform in an effort to maintain an active and actively data collecting community. When a physical activity is completed, the app offers to publish the produced and aggregated data for oneself or others to make sense of. The user can use this ‘raw’ data to determine ‘de facto’ knowledge about one’s body, as a way of understanding the self, both as a snapshot and historically. In this way, Strava can for some be seen as a “dashboard of the body” (Lupton 69), producing neutral and objective knowledge about one’s body.
Raw data as empiricism, what is data?
Anderson, former editor-in-chief for Wired Magazine, infamously stated that Big Data is marking the end of theory; that data now can speak truthfully for itself, rendering models and theories needless going forward (Anderson). Kitchin, in his article ‘Big Data, new epistemologies and paradigm shifts’ (Kitchin) has another take on this, stating that the empiricist epistemology (Pappas), which Anderson can be interpreted to refer to, is to be considered as attractive yet based on misleading principles, as “whilst data can be interpreted free of context and domain-specific expertise, such an epistemological interpretation is likely to be anaemic or unhelpful as it lacks embedding in wider debates and knowledge” (Kitchin 5). Data isn’t to be considered natural, or raw, and data is created “(…) within a complex assemblage that actively shapes its constitution” (Kitchin 4–5), whilst being “(…) shaped by the technology and platform used, the data ontology employed and the regulatory environment, and (…) subject to sampling bias” (Kitchin 4).
It has long been acknowledged that technology is inherently non-neutral (Winner), and the data which has been produced from it will always be shaped by the socio-technical contexts and relations in its creation and interpretation – Strava not exempted. As Dalton & Thatcher comment, technological and societal change are intertwined in a dialectic relationship, as “the innovation, production, and popular use of a technology occurs within and reflects a social context shot through with power, economies, identities, and biases. (…) A technology does not act alone, out of context, determining the form of society” (Dalton and Thatcher, sec.3).
Venturing briefly into the etymology of ‘data’ I take on Rosenberg’s definition, in that “facts are ontological, evidence is epistemological, data is rhetorical (…) When a fact is proven false, it ceases to be a fact. False data is data nonetheless” (Rosenberg 18). Data, then, can be said to be harnessed for something, be it economic value or knowledge production, and will thus bear that purpose implicitly in its creation and shaping. Data model structures and encodings of information have been designed by someone or something, which is also determining what should be considered as ‘proper’ data in terms of capture (Agre). This, then, comes to show that data cannot be considered ‘transparent’ or ontologically ‘true’, as provoked by Dalton & Thatcher, in that “what is quantified, stored, and sorted? What is discarded? All datasets are necessarily limited representations of the world that must be imagined as such to produce the meaning they purport to show” (Dalton and Thatcher, sec.4; Gitelman).
The outcome of this discussion would, in my opinion, render any claims of ‘the objectivity of data’ vulnerable towards critique, in the questioning of the producing of the data to begin with; the statistics I’m presented with at the end of a bike-ride tracked by Strava is also the result of the decisions made by Strava, as an assemblage, on what to track, how to track it, how to aggregate it, and how to present it, much akin to the notion of mechanical objectivity which Gitelman and Jackson exemplifies by the first photographic processes (5). This is by no means a claim that the gathered data is unusable nor irrelevant, but rather to confer the necessity of interrogating the data one is met with on an everyday basis, so that knowledge produced and actions taken on the behalf of such is promoted neither as neutral nor objective, but rather considered a product of a socio-technical assemblage, of which we as individuals take part in – perhaps even marked by some sort of discursive normative underpinnings.
Agre, Philip E. ‘Surveillance and Capture: Two Models of Privacy’. The NewMediaReader, edited by Noah Wardrip-Fruin and Nick Montfort, MIT Press, 2003, pp. 737–60. Library of Congress ISBN, https://monoskop.org/images/4/4c/Wardrip-Fruin_Noah_Montfort_Nick_eds_The_New_Media_Reader.pdf.
Anderson, Chris. ‘The End of Theory: The Data Deluge Makes the Scientific Method Obsolete’. Wired, June 2008. www.wired.com, https://www.wired.com/2008/06/pb-theory/.
Bowker, Geoffrey C. Memory Practices in the Sciences. MIT Press, 2005.
Dalton, Craig, and Jim Thatcher. ‘WHAT DOES A CRITICAL DATA STUDIES LOOK LIKE, AND WHY DO WE CARE?’ Society & Space, 12 2014, http://societyandspace.org/2014/05/12/what-does-a-critical-data-studies-look-like-and-why-do-we-care-craig-dalton-and-jim-thatcher/.
Gitelman, Lisa, editor. ‘Raw Data’ Is an Oxymoron. MIT Press, 2013.
Gitelman, Lisa, and Virginia Jackson. ‘Introduction’. ‘Raw Data’ Is an Oxymoron, edited by Lisa Gitelman, MIT Press, 2013, pp. 1–14.
Kitchin, Rob. ‘Big Data, New Epistemologies and Paradigm Shifts’. Big Data & Society, vol. 1, no. 1, July 2014, p. 205395171452848. DOI.org (Crossref), https://doi.org/10.1177/2053951714528481.
Lupton, Deborah. The Quantified Self: A Sociology of Self-Tracking. 2016. Open WorldCat, https://ezproxy.aub.edu.lb/login?url=https://ebookcentral.proquest.com/lib/aub-ebooks/detail.action?docID=4678321.
Manovich, Lev. The Language of New Media. MIT Press, 2001.
Pappas, George S. ‘Epistemology in the Empiricists’. History of Philosophy Quarterly, vol. 15, no. 3, 1998, pp. 285–302. JSTOR.
Pink, Sarah, et al. ‘Mundane Data: The Routines, Contingencies and Accomplishments of Digital Living’. Big Data & Society, vol. 4, no. 1, June 2017, p. 12. Crossref, https://doi.org/10.1177/2053951717700924.
Pink, Sarah, and Vaike Fors. ‘Being in a Mediated World: Self-Tracking and the Mind–Body–Environment’. Cultural Geographies, vol. 24, no. 3, July 2017, pp. 375–88. DOI.org (Crossref), https://doi.org/10.1177/1474474016684127.
Rosenberg, Daniel. ‘Data before the Fact’. ‘Raw Data’ Is an Oxymoron, edited by Lisa Gitelman, The MIT Press, 2013.
Winner, Langdon. ‘Do Artifacts Have Politics?’ Daedalus, vol. 109, no. 1, 1980, pp. 121–36.
Wolf, Gary. ‘What Is The Quantified Self?’ Quantified Self, 4 Mar. 2011, https://quantifiedself.com/blog/what-is-the-quantified-self/.