  A confluence of advances in the computer and mathematical sciences has
unleashed unprecedented capabilities for enabling true evidence-based decision
making. These capabilities are making possible the large-scale capture of data
and the transformation of that data into insights and recommendations in
support of decisions about challenging problems in science, society, and
government. Key advances include jumps in the availability of rich streams of
data, precipitous drops in the cost of storing and retrieving massive amounts
of data, exponential increases in computing power and memory, and jumps in the
prowess of methods for performing machine learning and reasoning. These
advances have come together to create an inflection point in our ability to
harness large amounts of data for generating insights and guiding decision
making. The shift of commerce, science, education, art, and entertainment to
the web makes available unprecedented quantities of structured and unstructured
databases about human activities - much of it available to anyone who wishes to
mine it for insights. In the sciences, new evidential paradigms and sensing
technologies are making available great quantities of data, via use of
fundamentally new kinds of low-cost sensors (e.g., genomic microarrays) or
through viewers that provide unprecedented scope and resolution. The data pose
a huge opportunity for data-centric analyses. To date, we have only scratched
the surface of the potential for learning from these large-scale data sets.
Opportunities abound for tapping our new capabilities more broadly to provide
insights to decision makers and to enhance the quality of their actions and
policies.
