Page 12 - GCN, August/September 2017

DATA ANALYTICS
ADVANCED ANALYTICS GET REAL
Government agencies are turning to data analytics to solve a growing range of problems.
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WHEN IT COMES to advanced analytics, technology is only part of the equation.
The ongoing evolution of analytic tools and methodologies is certainly making it easier for government agencies to develop, deploy, and
manage data initiatives.
Recent technology advances are enabling a “democratization”
of data, making advanced analytics accessible to non-data specialists. But such advances would be meaningless if agencies did not perceive the value of analytics—that is, the potential for analytics to address their most pressing problems.
new data source. That is the case in Chattanooga, Tenn., where a team of researchers plans to install sensors to give them real-time mapping of air pollution and allergen data that can affect people who suffer from asthma and allergies. That team is funded in part by the National Science Foundation.
The Emergence of IoT
An increasing amount of data initiatives are linked to emerging Internet of Things (IoT) applications—networked computers, sensors, and other devices automatically generating streams of data. Setting up IoT networks is easy. The real task is mining that data.
“It’s not about the technology,” says Kevin Garrison,
chief of analytics in the Office of the Special Assistant for
Governance and Analytics at the Department of Defense. “It’s
about understanding what question you’re trying to answer, Technology Research and Development Program (NITRD). what problem you’re trying to solve.” Garrison was speaking According to the report, by 2018, more than half of
“The IoT is a rapidly emerging source of big data,” states the Federal Big Data Research and Development Strategic Plan, published in May 2016 by the Networking and Information
at a recent event, “Analytics Supporting National Security: Advanced Capabilities for Better Decision-Making.”
Over the last year, government agencies have turned to data analytics to address a growing range of problems. Here is a look at three key factors driving the surge in analytics initiatives.
More Data, New Data Streams
Internet traffic will come from sensors and other devices, not necessarily computers. “New tools are needed to unify and organize this information into human- and machine-readable summaries in a timely fashion,” the report states.
Law enforcement agencies, among others, are investing in IoT in a big way. Investments in IoT-related equipment and services have tripled during the past six years, according to a recent study by Govini, a contracting intelligence firm.
When they made their initial forays into big data, many agencies
focused on leveraging their existing data stores. As those
initiatives have borne fruit, however, agencies are now looking to often as part of so-called smart city initiatives funded in
Many IoT applications are springing up at the local level,
build new data streams or tap into streams created by others. For example, in recent years, as part of its efforts to detect
part by federal grants. Unfortunately, the grassroots-style development of IoT applications could make it difficult
to integrate these efforts down the road. This could limit their scope and value, according to a recent report from the
disease outbreaks, the Centers for Disease Control and
Prevention has been studying international aviation data. They’re
looking at how air travel helps to spread communicable disease. Government Accountability Office (GAO). GAO auditors see
Now the agency is looking to step up its work, adding more data sources and more advanced analytic tools and making it all accessible through a unified web-based portal. The program will support U.S. and international public health organizations.
In some cases, government agencies are tapping into data sources that did not even exist a few years ago. The city of Boston, for example, is undertaking a new data initiative to assist with transportation planning. Potential sources include public transit organizations, commercial traffic data services, and even travel data from ride-sharing services, such as Uber.
In other cases, however, the potential benefits of data initiatives can easily justify investments required to create a
an opportunity for the federal government to help foster a more cohesive approach to IoT.
Augmented Analysis
IoT and other emerging data-intensive systems represent a new challenge for federal agencies. The volume and complexity of the data involved can make it difficult for human analysts to translate that data into intelligence. That’s where machine learning, artificial intelligence (AI), and related technologies come in. The idea is to develop software that can do a better job of connecting the dots—recognizing meaningful patterns and associations in data, and actually learning from its “experience” of doing so.
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