A law enforcement agency might use predictive analysis, looking at statistical data and algorithms, to determine crime rates, while a public health agency might use social network analysis to determine a disaster’s impact on different communities. Other forms of advanced analytics are geospatial analysis, text mining and network analysis.
Cybersecurity is a top federal priority, and agencies want to identify threats before breaches occur. To improve that capability inside networks, agencies must remove information sharing bottlenecks without compromising security.
Implemented properly, advanced analytics helps agencies identify organizational inefficiencies and leads to cost savings, but there are disadvantages as well.
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The Disadvantages of Advanced Analytics
Advanced analytics comes with a high upfront cost, and agencies’ budgets aren’t unlimited.
Data quality issues need to be addressed before implementing advanced analytics, and there’s no standard set of technologies for collecting, offloading and protecting data.
Deploying and maintaining the proper infrastructure for advanced analytics requires paying highly skilled personnel, which is the biggest hurdle of them all. Technical experts need deep familiarity with their agencies’ data and the analytics available to them.
Government left industry to drive development of advanced analytics capabilities and is now skeptical about adopting the technologies. Many people consider them intrusive due to the prevalence of cameras in countries such as the United Kingdom and the portrayal of video analytics on TV and in movies.