Big Data and Cloud Culture - To the Cloud: Big Data in a Turbulent World - page 188

at between $21.5 and $35 billion over the next three years as a result of
fears generated by NSA surveillance (Taylor 2013a). Cisco claimed that it
had already lost business in emerging markets because of concerns about
U.S. spying (Meyer 2013).
The government commitment to cloud computing is not limited to
military/intelligence applications. In addition to advancing research in
medicine and health care, it is looking to reduce healthcare costs, and the
analysis and predictive promise of big data are means of meeting this goal.
To that end the government is funding a joint project bringing together
the National Science Foundation and the National Institutes of Health
to research “managing, analyzing, visualizing, and extracting useful
information from large and diverse data sets” (U.S. Ofice of Science and
Technology Policy 2012). While improving the analysis and display of
data is not controversial, the ultimate goal of predicting outcomes based
on patient information has stirred concerns that government will use the
results to modify behavior in ways considered excessively intrusive. For
example, should the government tailor its medical-insurance coverage
to the health choices of Americans, with cuts to beneits for those who
make what the data suggests are bad choices? Another health-related
ield, genomics, is also a popular subject in big-data discussions. Here
the government is teaming with Amazon Web Services (AWS), which
helped bring victory to President Obama in the 2012 election, to store
200 terabytes (16 million ile cabinets or 30,000 standard DVDs) of data
from genomics research. The data is publicly available, but users have to
pay AWS for computing costs. It is interesting to observe another example
of the government's dependence on private cloud companies, in this case
one of the most important in the world, to store, process, and distribute
valuable data sets. Finally, energy and geology research receive funding
to advance the capacity of these ields to analyze, visualize, and predict
the behavior of resource and geological systems.
Big data is increasingly used in the traditional social sciences and in
the humanities. Social-science research is now often conducted by private
corporations that see signiicant opportunities in areas such as real-time
fraud detection, health risk assessments for medical patients, continuous
process monitoring of consumer sentiment or vital mechanical systems,
and network relationships on social-media sites (Davenport, Barth, and
Bean 2012). Large data sets are providing new opportunities for research