Big Data Poses Challenges For Federal Agencies Posted on : Oct 09 - 2015

While government officials say that advanced analytics have improved the speed of decision-making in many different federal agencies, these leaders also express plenty of concern about a range of issues surrounding big data. That's according to new survey commissioned by global IT services provider Unisys.

While it's not intended to be a complete picture, the Unisys survey, which culls responses from about 100 government officials, provides a glimpse inside the state of big data and analytics programs in the federal government.

The US government has increased its big data play over the last few years. The Obama administration in 2012 committed $200 million to leveraging big data to drive actionable insights. In February the White House announced the appointment of US Chief Data Scientist DJ Patel, the first person with that title.

How Government Is Using Big Data

More than 60% of agencies are using big data to reduce costs including capital and operating expenses. For example, Unisys said, agencies can find incorrect invoices or payments that may have been made erroneously. Taking it a step further, they can use root-cause analysis to determine what caused those errors and correct flaws in the system.

In addition, 55% of agencies are using big data to improve their IT security, according to the survey. For instance, some are using analytics to detect advanced threats by automating the identification of inconsistencies in machine data. That process gives agencies better insight into how attacks happened. It can even enable them to stop ongoing attacks, Unisys said.

But US government executives are grappling with many of the same issues and concerns as their counterparts in the private sector are when it comes to big data, according to the Unisys survey.

For instance, one of the survey's findings is that while some agencies are far along the path to realizing the benefits of big data, other agencies "seem somewhat overwhelmed about the expertise and expense required to make the most of big data."  View More