Predictive analytics to transform government business processes

While predictive analytics technology is not a new phenomenon, these solutions have not yet fully matured. Many IT experts believe that, within the next few years, a growing number of organizations will begin to leverage these solutions in a more diverse range of capacities.

Some of the most notable applications of predictive analytics will likely be found among government agencies. As FCW contributor Thom Rubel recently highlighted, predictive analytics has the potential to greatly transform business process management among many federal and state agencies.

Predictive potential
Rubel explained that there are many scenarios in which predictive analytics could have a significant, positive impact on government operations. He argued that agencies can utilize current and historical data to identify trends which in turn will lead to better, more informed decision-making.

"For example, programs that are collectively designed to ensure the smooth flow of people and commerce are typically informed by multiple data sources generated by people or things (sensors, data networks, etc.)," Rubel wrote. "Predictive decision-making ensures that the right combinations of information come together based on business rules that optimize desired outcomes – think smooth traffic flows."

The potential viability of predictive analytics is further enhanced by the development of the Internet of Things, the author explained. Government data networks will soon incorporate information from a tremendous variety of sources, from cars to utilities to biometric and health-monitoring devices. This will provide an ever-growing amount of information for government agencies to utilize for accurate predictive capabilities.

"Eventually, traditional government business processes will be replaced – or deeply aided – by a system of Internet-of-Things devices," Rubel asserted.

Challenges remain
However, for the government to take advantage of these data streams and analytics capabilities, there are several challenges that must first be overcome. First, as Rubel explained, agencies will need the tools and strategies to effectively manage the ever-increasing amount of data available to them. Without solutions in place, it will be difficult, if not impossible, for agencies to gain meaningful insight from such overwhelming volumes of information.

Additionally, government agencies will need talented IT personnel to implement advanced predictive analytics solutions. Currently, the government struggles to attract and retain top IT talent, due to the lack of incentives and high degree of bureaucracy. If agencies cannot overcome this hurdle and find ways to appeal to skilled experts, the only option remaining may be to increasingly outsource data management and analytics efforts.