Page 9 - Cyber Warnings

environment in general). These requirements should be based upon the data assets with the
highest sensitivity level, as well as the sensitivity levels of potential combinations of the data
(which can lead to unanticipated sensitive results). The requirements will, in turn, enable the
definition of the appropriate security controls that need to be integrated into any system to
reduce risk and prevent possible breaches. Another approach is to plan on using more than one
system to separate sensitive and non-sensitive data. By compartmentalizing data by sensitivity
level, your organization will reduce the risk—and cost—of implementing unnecessary security
controls that might otherwise inhibit comprehensive data analysis. Finally, the organization
needs to consider technology solutions to perform Big Data analysis (such as the analytics,
visualization, and warehousing tools). The solutions selected—including infrastructure, core
solutions and any “accelerators”—should be based upon the information collected up to this
point, including security requirements, data structures (structured vs. semi-structured vs.
unstructured) and data volume. Remember, no single technology is required for a “Big Data
Solution.” Instead, it should be based upon specific requirements. Data scientists can then use
these technologies to develop algorithms to process the data and interpret the results. Once
completed, the business can move on to the next use case. In summary, it is essential to
remember that communication, change management and governance are key to successfully
deriving any meaningful and usable results from Big Data. Other key tips that will lead to
success include:

 Do not start with a focus on technology. Instead, focus on business/mission
requirements that cannot be addressed using traditional data analysis techniques.
 Augment existing IT investments to address initial use cases first; then, scale to support
future deployments.
 Architect a secure environment for analyzing public streams and private data sources.
 After the initial deployment, expand to adjacent use cases, building out a more robust
and unified set of core technical capabilities.

These factors will ensure that the organization adopts Big Data securely and effectively,
achieving results at each iterative step and maximizing the use of your valuable resources.

About the Author
Branko Primetica is Chief Strategy Officer of eGlobalTech, a leading
management and IT consulting firm supporting the U.S. Government.
He has more than 15 years of experience supporting Federal
Government Agencies and Fortune 500 companies with enterprise-
wide business transformation. His experience also includes advising
international audiences/organizations on emerging technologies and
their practical application—including Big Data. Branko is a partner at eGlobalTech and helped
the company grow into a recognized market leader in innovation, agile development and cyber
security across the public sector. eGlobalTech’s support includes Big Data implementations at
the Departments of Health and Human Services, Homeland Security and Commerce. Branko
can be reached online at [email protected] and on the company website
www.eglobaltech.com.
9 Cyber Warnings E-Magazine – February 2016 Edition
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