As government agencies continue to embrace big data, many have only taken the first step, such as implementing a Hadoop or NoSQL database. While these databases may offer a more efficient or cost-effective means to process large data sets, it's still static data. On its own, Hadoop doesn't offer a means for real-time decision-making.

New streaming analytics tools that weren't available until recently stand to deliver the real-time, perishable insights agencies need to take preventative action, and not just react after the fact when data has lost value.

 They need systems designed to process high-velocity data flows from innumerable real-time data sources such as the Internet of Things, mobile apps, sensors, clickstream, and even transaction data that has largely been untapped by most agencies. They must blend real-time data with static data in order to dramatically enhance their real-time decision-making capabilities.

Once the technology is in place, two common data categories are necessary for collection: static and streaming. Static data is stored in a variety of ways, from traditional database or data warehouse to Hadoop clusters or NoSQL databases. The analysis is run on this stored data. Streaming data refers to real-time data -- information pouring in from sensors, devices, or social media that is not worth storing due to its perishable nature. In order to maximize the benefits of all of this data, agencies must be able to run analytics on both their static and streaming data.

Source: http://www.informationweek.com