“From the beginning, data was a rhetorical concept. “Data” means that which is given prior to argument. As a consequence, its sense always shifts with argumentative strategy and context—and with the history of both. The rise of modern natural and social science beginning in the eighteenth century created new conditions of argument and new assumptions about facts and evidence. But the pre-existing semantic structure of the term “data” gave it important flexibility in these changing conditions. It is tempting to want to give data an essence, to define what exact kind of fact it is. But this misses important things about why the concept has proven so useful over these past several centuries and why it has emerged as a culturally central category in our own time. When we speak of “data,” we make no assumptions about veracity. It may be that the electronic data we collect and transmit has no relation to truth beyond the reality that it constructs. This fact is essential to our current usage. It was no less so in the early modern period; but in our age of communication, it is this rhetorical aspect of the term that has made it indispensable” (“Data before the Fact”, by D. Rosenberg, 2012).

http://courses.ischool.berkeley.edu/i218/s12/Rosenberg.Data..draft.pdf

“The Origins of ‘Big Data’ : An Etymological Detective Story”
(by Bits, The New York Times)

http://bits.blogs.nytimes.com/2013/02/01/the-origins-of-big-data-an-etymologi…

“The paper approaches Twitter through the lens of “platform politics” (Gillespie, 2010), focusing in particular on controversies around user data access, ownership, and control. We characterise different actors in the Twitter data ecosystem: private and institutional end users of Twitter, commercial data resellers such as Gnip and DataSift, data scientists, and finally Twitter, Inc. itself; and describe their conflicting interests. We furthermore study Twitter’s Terms of Service and application programming interface (API) as material instantiations of regulatory instruments used by the platform provider and argue for a more promotion of data rights and literacy to strengthen the position of end users” (Puschmann and Burgess, 2013)

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2206225

Note 1: from a logic of “set” to a logic of “emergence”

“So-called #Bigdata are not relevant because they support a “micro-segmentation” of customers. This is the trivial discourse about the “data-intensive” marketing. This outdated perspective depends on the persistent vision of a marketing approach based on the logic of “set” (micro or macro, it does not matter). My viewpoint is that we are currently (but not consciously) shifting to a new logic: the logic of the “emergence”. Ontologically speaking, “customer” does not belong to a micro-segment (to be/not to be an element of a set); instead, the probability to be a “customer” emerges and varies (microsecond by microsecond) according to the computational and scoring capabilities of customer databases and devices to produce a modulated, temporally and spatially situated and embodied, “dividuality” (Cosimo Accoto, 2013)

“The article uses an established three-dimensional conceptual framework to systematically review literature and empirical evidence related to the prerequisites, opportunities, and threats of Big Data Analysis for international development. On the one hand, the advent of Big Data delivers the cost-effective prospect to improve decision-making in critical development areas such as health care, employment, economic productivity, crime and security, and natural disaster and resource management. This provides a wealth of opportunities for developing countries. On the other hand, all the well-known caveats of the Big Data debate, such as privacy concerns, interoperability challenges, and the almighty power of imperfect algorithms, are aggravated in developing countries by long-standing development challenges like lacking technological infrastructure and economic and human resource scarcity. This has the potential to result in a new kind of digital divide: a divide in data-based knowledge to inform intelligent decision-making. This shows that the exploration of data-based knowledge to improve development is not automatic and requires tailor-made policy choices that help to foster this emerging paradigm”

“Social media data typically consists of non-curated, short messages that are shared among people, instead of being visited manually or crawled by an automatic agent. Messages may be distributed through an explicit network of friends and followers, openly visible or privately, according to the sender’s preferences. There is no publishing delay and the barrier of entry is low, often only requiring an email address. This leads to substantial volumes of content constantly being created, and an expectation of data currency. Online social networks are a source of “big data”. Our social graphs are made explicit; our interactions are recorded; our utterances are saved in machine readable format; we can be heard across the world as easily as across the room. Twitter alone generates a million messages every ???ve minutes; a four-day stream comprises around 10(9) messages. As a result of online social networking, massive volumes of diverse social media data that capture a sample of all human discourse are accessible online”

http://www.derczynski.com/sheffield/papers/scalable_sm.pdf

“Big Data, data mining, and analytics at this point are lightning rods for both the promise of digital technologies and the uncertainty surrounding their implications for the future. (…) In analytic operations, algorithms (statistical computations with clearly defined steps) are central, having attained new significance in searches for meaning in digital depositories. They are involved in sense-making (pattern detection) as well as in meaning-making (pattern building) through existing recognition algorithms and by actively constructing algorithms that will lead to a desired outcome. Typical data mining requires multiple, conjoined sets of algorithms and multiple iterations during which the correct series of steps is determined. Algorithms are a crucial feature of the digital transformation. But it is important to remember that they are not neutral; they have a language and a politics. They incorporate a certain worldview.In analytics, we are dealing with a concatenation of different algorithms whose relationships and assumptions interact and quickly become untraceable. Ethnographers need to understand what kinds of algorithms affect their research and what interests, technical knowledge, and resources drive their construction. Significantly, we often do not know

(from Jordan, ed. “Advancing Ethnography in Corporate Environments: Challenges and Emerging Opportunities”, 2013) 

“Interest in Big Data analytics (BDA) has certainly skyrocketed in the past few years to reach a fevered pitch, with the market for this technology projected to reach a 58% compounded annual growth rate over the next five years.1 Indeed, when I walked the vendor exhibit halls at several TDWI World Conferences during the past year, it seemed that nearly all the application vendors had introduced a new package offering a “Big Data” solution. At every booth, plenty of curious attendees lined up to hear about these new features. The vendors were certainly happy for the attention, but they also confided to me that they had grown tired of answering the same question day after day, namely “What is Big Data?”

here a link 
http://www.cutter.com/content/itjournal/fulltext/2012/10/index.html

[…] According to Gray, we are seeing the evolution of two branches in every discipline: a computational branch and a data-processing branch. For example, in ecology there is now “both computational ecology, which is to do with simulating ecologies, and eco-informatics, which is to do with collecting and analyzing ecological information” (xix). How will the social sciences be affected by these developments? This chapter aims to contribute to a better understanding of the implications of data-intensive and computational research methodologies for the social sciences by focusing on two social science fields: sociology and economics. We address the implications of this debate for sociology and economics by uncovering what is at stake here. Although different kinds of “new data” are collected by both disciplines (transactional versus brain data), they serve as good examples to demonstrate how disciplines are responding to the availability of new data sources”
 
(from “SLOPPY DATA FLOODS OR PRECISE SOCIAL SCIENCE METHODOLOGIES? DILEMMAS IN THE TRANSITION TO DATA-INTENSIVE RESEARCH IN SOCIOLOGY AND ECONOMICS”,

in Virtual Knoowledge, The MIT Press, 2013)