Six Provocations for Big Data
For those who can think beyond the Hadoop or related Big Data technologies, here is a set of definitive Big Data Provocators – they will matter when you have mastered the technology of big data handling — and start looking at their socio-commercial impacts:
Big Data is notable not because of its size, but because of its relationality to other data. Due to efforts to mine and aggregate data, Big Data is fundamentally networked. Its value comes from the patterns that can be derived by making connections between pieces of data, about an individual, about individuals in relation to others, about groups of people, or simply about the structure of information itself.
http://sonic.northwestern.edu/six-provocations-for-big-data/ (Posted in: Networks in the News by Hugh Devlin)
Paper by danah boyd of Microsoft Research and Kate Crawford of the University of New South Wales, presented at Oxford Internet Institute’s “A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society” on September 21, 2011. Here’s a nutshell summary of the six provocations:
1) Big Data heralds the computational turn in thought and research – akin to Ford’s assembly line. Automating Research Changes the Definition of Knowledge.
2) Big Data is not self-explanatory. And yet the specific methodologies for interpreting the data are open to all sorts of philosophical debate.
3) Bigger Data are Not Always Better Data. There is a problematic underlying ethos that bigger is better, that quantity necessarily means quality.
4) Not All Data Are Equivalent. Because data is not generic. There is value to analyzing data abstractions, yet the context remains critical.
5) Just Because it is Accessible Doesn’t Make it Ethical to use the data. Very little is understood about the ethical implications of the research being done and significant questions of truth, control and power in Big Data studies. And recognize the fact that there is very little difference between “being in public and being public”
6) Limited Access to Big Data Creates New Digital Divides: Big Data rich and Big Data poor
“How can students be educated so that they are equally comfortable with algorithms and data analysis as well as with social analysis and theory?” (Source: http://sonic.northwestern.edu/six-provocations-for-big-data/)
Get the whole paper at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1926431
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