Author: Cosimo Accoto

“Tomorrow successful product designers will understand that their creations can never again exist in isolation. They will need to survive on a network. They must switch effortlessly between serving end users and developers equally well. They will need to deeply internalize that their products are nothing but data. But with this challenge comes an unprecedented opportunity to radically alter the very definition of what a product is, as well as the value that it can provide to the world. Pip Coburn, author of the book The Change Function, argues that people today feel naked without data. That is, we have become so used to having a universe of information in our pocket/purse/bag in the form of a mobile phone that when we are without it, we feel vulnerable, unprepared, or even disrobed. The fascinating thing about this is that the idea makes sense only if you think about it terms of online data. In reality, you’re surrounded by information and data 24/7; you have been since you were born. It’s information made available through your five senses (and sometimes your sixth’s that sense of intuition we all have and that’s especially strong in mothers). But for some reason, that type of data is boring to a lot of people at least for the moment. On the other hand, the data available via LinkedIn, Twitter, or Yelp is far more interesting (for now). Elevating social machines to the level of social peers can and will change this; these social machines will become both conduit and catalyst” (from Semmelhack, “Social Machines”, 2013).

“Today, people analytics is poised for a revolution, and the catalyst is the explosion of hard data about our behavior at work. This data comes from a wide variety of sources. Digital traces of activity from e-mail records, web browsing behavior, instant messaging, and all the other IT systems we use give us incredibly detailed data on how people  work. Who communicates with whom? How is IT tool usage related to productivity? Are there work styles that aren’t well-supported by current technology? Although this data can provide amazing insights, it’s only the digital part of the story. Data on the physical world is also expanding at a breakneck pace thanks to the rapid development of wearable sensing technology. These sensors, from company ID badges to cell phones to environmental sensors, provide reams of fine-grained data on interaction patterns, speaking patterns, motion, and location, among other things. Because most communication and collaboration happens face to face, this data is critical for people analytics to take that next leap forward and become a transformative organizational tool. By combining precise data from both real and virtual worlds, we can now understand behavior at a previously unimaginable scale” (from the Preface to “People Analytics”, Waber, FTPress, 2013)

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“Database development has followed this vein. The early databases were hierarchical – you needed to go down a detailed line of authority each time you wanted to retrieve a datum. Then we had relational databases, where there was still central control but much more flexible access (the database system, like society at the time, was seen as a fixed structure). Today we have moved into a world of object-oriented and object-relational databases, in which each data object lives in a Tardean paradise — any structure can be evanescent providing we know the inputs or outputs of any object within it. So databases have recapitulated social and organizational developments. And many organizations changed in the 1990s and 2000s in an effort to become more “ object-oriented ” ; forgetting that the first object-oriented language (Simula, a precursor to Smalltalk) attempted to model work practice. Along the way, we have conceived ourselves and the natural entities in terms of data and information. We have flattened both the social and the natural into a single world so that there are no human actors and natural entities but only agents (speaking computationally) or actants (speaking semiotically) that share precisely the same features. It makes no sense in the dataverse to speak of the raw and the natural or the cooked and the social: to get into it you already need to be defined as a particular kind of monad” (“Data Flakes”, Geoffrey C. Bowker, in “Raw Data is an Oxymoron”, 2013, The MIT Press, p. 169)

“This provocation piece addresses the recent rise of algorithms as an object of interest in research, policy, and practice. It does so through a series of provocations that aim to trouble the coherence of the algorithm as an analytic category and to challenge some of the assumptions that characterize current debates. The goal of this piece is thus to stimulate discussion and provide a critical backdrop against which the Governing Algorithms conference can unfold. It asks whether and how we can turn the “problem of algorithms” into an object of productive inquiry” (Barocas et alii, 2013).

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2245322

Abstract: Digital technologies keep track of everything we do and say while we are online, and we spend online an increasing portion of our time. Databases hidden behind web services and applications are constantly fed with information of our movements and communication patterns, and a significant dimension of our lives, quantified to unprecedented levels, gets stored in those vast online repositories. This article considers some of the implications of this torrent of data for social science research, and for the types of questions we can ask of the world we inhabit. The goal of the article is twofold: to explain why, in spite of all the data, theory still matters to build credible stories of what the data reveal; and to show how this allows social scientists to revisit old questions at the intersection of new technologies and disciplinary approaches. The article also considers how Big Data research can transform policy making, with a focus on how it can help us improve communication and governance in policy-relevant domains (Gonzalez-Bailon, 2013)

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2238198

According to Bonenfant et alii (Big Data and Governance, 2013) “immediacy” and “totality” are the two “ideological” dimensions of the “big data” discorse. Collecting thoughts for my next sketch .. 😉

[ from the introduction] “Algorithms are widely recognized as playing an increasingly influential role in the political, economic, and cultural spheres (Mayer-Schonberger, & Cukier, 2013; Pariser, 2011; Steiner, 2012). Algorithms are serving particularly prominent roles in the media sector, where the processes of media production, consumption, and even advertising placement, are increasingly automated and algorithmically dictated (see, e.g., Danaher, Lee, & Laoucine, 2010; Mager, 2012; Steiner, 2012). Clearly, then, the algorithmic turn (to borrow Uricchio’s [2011] phrase) that is taking place in the media sector should be a focal point for communication and media studies scholarship. Researchers have begun to examine this transition in a number of contexts and from a variety of analytical perspectives (see, e.g., Beer, 2001; Gillespie, 2011; Webster, 2011). But, as is to be expected in these early stages of an emergent area of inquiry, there has been relatively little discussion of useful theoretical frameworks (for exceptions, see Anderson, in press; Webster, 2011). This paper attempts to address this gap via an exploration of how institutional theory can inform and guide future research on the algorithmic turn in media production and consumption, as well as provide a lens through which to interpret extant research in this area” (Philip M. Napoli, May 2013)

Napoli, Philip M., The Algorithm as Institution: Toward a Theoretical Framework for Automated Media Production and Consumption (May 5, 2013).

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2260923

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[Abstract] “As part of the digital health phenomenon, a plethora of interactive digital platforms have been established in recent years to elicit lay people’s experiences of illness and healthcare. The function of these platforms, as expressed on the main pages of their websites, is to provide the tools and forums whereby patients and caregivers, and in cases medical practitioners, can share their experiences with others, benefit from the support and knowledge of other contributors and contribute to large aggregated data archives as part of developing better medical treatments and services and conducting medical research. However what may not always be readily apparent to the users of these platforms are the growing commercial uses by many of the platforms’ owners of the archives of the data they contribute. This article examines this phenomenon of what I term ‘the digital patient experience economy’. In so doing I discuss such aspects as prosumption, the phenomena of big data and metric assemblages, the discourse and ethic of sharing and the commercialisation of affective labour via such platforms. I argue that via these online platforms patients’ opinions and experiences may be expressed in more diverse and accessible forums than ever before, but simultaneously they have become exploited in novel ways” (Debora Lupton, 2013)

Deborah Lupton (2013) The Commodification of Patient Opinion: the Digital Patient Experience Economy in the Age of Big Data. Sydney Health & Society Group Working Paper No. 3. Sydney: Sydney Health & Society Group. 

“Like events imagined and enunciated against the continuity of time, data are imagined and enunciated against the seamlessness of phenomena. We call them up out of an otherwise undifferentiated blur. If events garner a kind of immanence by dint of their collected enunciation, as Hayden White has suggested, so data garner immanence in the circumstances of their imagination. Events produce and are produced by a sense of history, while data produce and are produced by the operations of knowledge production more broadly. Every discipline and disciplinary institution has its own norms and standards for the imagination of data, just as every field has its accepted methodologies and its evolved structures of practice. Together the essays that comprise “ Raw Data ” Is an Oxymoron pursue the imagination of data. They ask how different disciplines have imagined their objects and how different data sets harbor the interpretive structures of their own imagining. What are the histories of data within and across disciplines? How are data variously “ cooked ” within the varied circumstances of their collection, storage, and transmission? What sorts of conflicts have occurred about the kinds of phenomena that can effectively — can ethically — be “ reduced ” to data?” (ed. Lisa Gitelman, 2013, “Raw Data is an Oximoron”, The MIT Press, p. 3)