Big Data Week 9: Big data histories and epistemologies - ( Foucault is…
Big Data Week 9: Big data histories and epistemologies -
I think one of the
of using this text in the first place: Is showing that
the desire to visualise data or visualise information, has long existed and 'where we are today' is a product of the questions we asked before, to get 'here'
Wasn't hugely useful: But it did highlight the main point that 'visualisations' did exist and were evolving for many years (at least in this case, since 1800's).
Beer (2016) How should we do the history of Big Data?
Focus of the paper
: how we might develop a sociologically informed history of Big Data
One of two main arguments of the text
: we should approach this history (the history of big data) by treating Big Data as both a material phenomenon and also a concept
'History of Big Data'
: we already have a history of Big Data that can be found in accounts of the history of the use of statistics to know and govern populations (see for example Desrosie`res, 1998; Foucault, 2007; Hacking 1990; MacKenzie, 1981; Porter, 1986, 1995; Elden, 2007). For example there was an ‘avalanche of numbers’ that occurred around 1820 to 1840 as a new ‘enthusiasm for numbers’. Porter (1986: 11) observes, the ‘great explosion of numbers that made the term statistics indispensable occurred during the 1820s and 1830s’
One of Beer's main points
: we now need to work through a detailed account of what might be thought of as the birth of Big Data.
Why are we, and did we, become so obsessed with statistics?
It is this very appearance of neutrality that lends them an air of authority and which makes them so powerful.
So, Big Data can be placed within this long history of social statistics
One of the main points
: The point here is that we can only understand certain social phenomena through their discursive and conceptual formulations, and we can only understand these conceptual formula- tions by thinking historically about them.
Foucault is interested in
exploring the ways in which truth is produced so as to see how those truths limit understandings, actions and practices. When these 'truths' come to be, they also have implications for our 'actions' in this case - data analysis (for example).
Nice point: Data is seen, in this formation, to be unquestionable, accurate and over-arching in its panoramic view of the social world.
Some main conclusions of the text
: we have relatively little appreciation of how concepts make up those data. We need to look at the emergence of this powerful concept and to understand how it has been shaped and reshaped in its use. The power of Big Data is not just in the data themselves, it is in how those data and their potential is imagined and envisioned.
Boyde and Crawford reading: CRITICAL QUESTIONS FOR BIG DATA Provocations for a cultural, technological, and scholarly phenomenon
Given the rise of Big Data as a socio-technical phenomenon,
we argue that it is necessary to critically interrogate its assumptions and biases
In this article,
we offer six provocations to spark conversations about the issues of Big Data
: a cultural, technological, and scholarly phenomenon that rests on the interplay of technology, analysis, and mythology that provokes extensive utopian and dystopian rhetoric.
Nice quote: "Raw data is both an oxymoron and a bad idea; to the contrary, data should be cooked with care." (Bowker 2005, pp. 183–184). "Very little is understood about the ethical implications underpinning the Big Data phenomenon."
Computerized databases are not new. With the increased automation of data collection and analysis – as well as algorithms that can extract and illus- trate large-scale patterns in human behavior
‘Change the instruments, and you will change the entire social theory that goes with them’, Latour (2009) reminds us (p. 9). it is a profound change at the levels of epistemology and ethics.
With enough data, the numbers speak for themselves. (2008)
Do numbers speak for themselves? We believe the answer is ‘no’. This does not mean that combining data does not offer valuable insights. aken out of context, data lose meaning and value.
But who gets access? For what purposes? In what contexts? And with what constraints? It is also important to recognize that the class of the Big Data rich is reinforced through the university system: top-tier, well-resourced universities will be able to buy access to data, and students from the top universities are the ones most likely to be invited to work within large social media companies.
Makes the point that he was intimidated almost by the extremity and size of data available. Calling on other people to assist in (counting the amount of breaths taken by an elephant, for example).