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Seeing and talking about Big Data Farida Vis, University of
Sheffield @flygirltwo
He created this installation that was at the Tate museum in
London a while back and the installation was these hundreds of
thousands of ceramic hand-painted sunflower seeds... And as you
stood back from the room it looked like this sea of just stones
that were black stones that were spread across the floor and of
course you couldnt really tell what they were. But as you got
closer it looks like, you can start to tell ooh it looks like
theyve stamped out hundreds of thousands of sunflower seeds and
spread them across the floor. But as you pick them up you started
to realise that they were all individually shaped and painted
differently and unique and beautiful and distinct in their own
right. So thats what we want to bring to what were building: the
ability to shrink the world and allow everybody to see each other.
Dick Costolo Twitter CEO, 2012 (quoted in Vis, 2012)
Synoptic view (Scott, 1998) a) Everything can be seen b)
Everything can be comprehended
A critical reflection on Big Data: considering APIs*,
researchers and tools as data makers *Application Programming
Interfaces
Data companies, structures, algorithms
Data companies Structures Algorithms APIs Researchers
Tools
Academic definition Big data includes cultural and
technological aspects, but also highlights Big Data as a scholarly
phenomenon, which rests on interplay between: Technology:
maximizing computation power and algorithmic accuracy to gather,
analyze, link, and compare large data sets. Analysis: drawing on
large data sets to identify patterns in order to make economic,
social, technical, and legal claims. Mythology: the widespread
belief that large data sets offer a higher form of intelligence and
knowledge that can generate insights that were previously
impossible, with the aura of truth, objectivity, and accuracy.
(boyd and Crawford, 2012, p. 663).
Industry definition Big data is high-volume, -velocity and
-variety information assets that demand cost-effective, innovative
forms of information processing for enhanced insight and decision
making (Gartner in Sicular, 2013). Part one: three Vs high Volume,
-Velocity, -Variety Key focus on processing data in real time. Part
two: highlight cost-effectiveness and innovation in processing this
data. Part three: key benefit is the possibility of greater insight
and thus better decision-making
Important to make visible inherent claims about objectivity
Problematic focus on quantitative methods How can data answer
questions it was not designed to answer? How can the right
questions be asked? Inherent biases in large linked error prone
datasets Focus on text and numbers that can be mined
algorithmically Data fundamentalism
Crawford (2013): data fundamentalism, the notion that
correlation always indicates causation, and that massive data sets
and predictive analytics always reflect objective truth. Idea and
belief in the existence of an objective truth, that something can
be fully understood from a single perspective, again brings to
light tensions about how the social world can be made known.
Barthes (1957) on myth: naturalize beliefs that are contingent,
making them invisible, and therefore beyond question. Bowker and
Star (2000): limitations of available ways in which information can
be stored in society. Instead of seeing the limitations of the
technical affordances and imagine different ways in which
information might be structured, the ways in which information is
structured become naturalized, people begin to see these structures
as inevitable (p. 108).
Data we want, but cant have
Amazon awarded Social Networking System patent (The United
States Patent and Trademark Office, 15 June 2010) "A networked
computer system provides various services for assisting users in
locating, and establishing contact relationships with, other users.
For example, in one embodiment, users can identify other users
based on their affiliations with particular schools or other
organizations. The system also provides a mechanism for a user to
selectively establish contact relationships or connections with
other users, and to grant permissions for such other users to view
personal information of the user. The system may also include
features for enabling users to identify contacts of their
respective contacts. In addition, the system may automatically
notify users of personal information updates made by their
respective contacts."
Perfect recommendation storm?
Data we dont know is collected
How can we talk about this? James Bridle (2013)
How to imagine future when present is hard to see?
Surveillance
More surveillance
Predictive policing
Seeing the matrix
Rise of the machines. No humans?
What does the internet look like?
Like this?
Or this?
Sadly more like this
And this
What does the cloud look like?
Facebooks Swedish data centre
Inside
Skynet
The cloud is not an object but an experience and its particles
are the very building blocks of a molecular aesthetic in which we
live and act (CFP for The Transdisciplinary Imaging
Conference)
How can we make algorithms visible?
What does the algorithm look like?
What/how does the algorithm see?
Is lady = wants baby
The human algorithm tension There are people in the machine 350
million images daily on FB
From around May 1996, just before Amazons IPO: Soon, Amazons
human editors were recommending books to customers based on similar
purchases they had made in the past. Amazon wasnt just a selling
site; it became an early social network site for book fans.
(Brandt, 2011, p. 86)
Trent Reznor: Chief creative officer at Daisy "What's missing
is a system that adds a layer of intelligent curation . . . As
great as it is to have all this information bombarding you, there's
a real value in trusted filters. It's like having your own guy when
you go into the record store, who knows what you like but can also
point you down some paths you wouldn't necessarily have
encountered. (from:
http://www.rollingstone.com/music/news/trent-reznor-named-creative-chief-of-beats-daisy-music-
service-20130110)
Finding new ways to see and talk about Big Data In particle
physics, one of the bedrocks of Big Data in the natural sciences,
so called dark matter cannot directly be seen or observed by
telescopes. Its presence can however be inferred by the
gravitational effects it has on visible matter, specifically
through the use of electromagnetic radiation. Drawing on particle
physics, we can however adopt a similar approach and aim to make
this unseen data and algorithmic structures visible by examining
data that can be seen. Through such an examination we can infer and
find out more about the dark matters gravitational effects on this
visible data and learn more about the dark matter itself.
References Roland Barthes, 1993 [1957]. Mythologies, London:
Vintage Classics. Brandt, R.L., (2011), One Click: Jeff Bezos and
the rise of Amazon. London: Portfolio Penguin James Bridle, 2013.
Naked Lunch Keynote presentation, Media Evolution Conference 2013
Malmo, Thursday 22nd August, http://bambuser.com/v/3836761 Geoffrey
C. Bowker and Susan Leigh Star, 2000. Sorting Things Out:
Classification and its Consequences. Cambridge, Massachusetts and
London, England: MIT Press. danah boyd and Kate Crawford, 2012.
Critical Questions for Big Data, Information, Communication &
Society, volume 15, number 5, pp. 662-679. Kate Crawford, 2013. The
Hidden Biases in Big Data, Harvard Business Review, HBR Blog
Network, 1 April, at
http://blogs.hbr.org/2013/04/the-hidden-biases-in-big-data/,
accessed 10 September 2013. John C. Scott, 1998. Seeing Like a
State: How Certain Schemes to Improve the Human Condition Have
Failed. New Haven and London: Yale University Press. Svetlana
Sicular, 2013. Gartner's Big Data Definition Consists of Three
Parts, Not to Be Confused with Three Vs, Forbes, 27 March, at
http://www.forbes.com/sites/gartnergroup/2013/03/27/gartners-big-data-definitionconsists-of-
three-parts-not-to-be-confused-with-three-vs/ , accessed 18 August
2013. Farida Vis, 2012a. Twitter brings you closer: the importance
of seeing the little data in Big Data, In: Drew Hemment and Charlie
Gere (editors). FutureEverybody: FutureEverything Report, pp. 43-
45, at http://futureeverything.org/FutureEverybody.pdf, accessed 10
September 2013.