In the past, when the US Department of Defense (DOD) sensors collected information, this information was first processed (forwarded to systems and people for analysis) then later posted (once the analytic product was finalized). The users (planners, warfighters, etc.) could not access the information until processing was completed and the final product posted.
Waiting for analysis and finished work products took too long – this resulted in varying degrees of strategic and operational error (e.g., bombs dropped in the wrong place). It became obvious this strategy must be changed.
As part of its network-centric warfare strategy, the DOD established the principle of "posting before processing." This means the data is made available to the users at the moment it becomes available. Analysts get this information at the same time and when their work product is finalized, it is then posted as additional information. By posting before processing, users have been able to make real-time operational decisions based on more current, albeit raw and uncorrelated, information. This has been hugely successful.
And while this transformation was well executed and a big step forward, I envision the next giant step forward will involve processing and posting at the same time. This means as theater sensors collect information, this information will be immediately placed into context with the historical data previously collected, previous analytic work products, open source and so on. This rich context will be constructed in real-time and posted in real-time.
From a network-centric warfare point of view – processing and posting at the same time is a form of Perpetual Analytics.
When DOD is able to process and post simultaneously, two very critical operational capabilities will emerge: 1) Persistent Context will be available to the user providing a uniquely comprehensive real-time operational picture, and 2) more importantly, as sensor data arrives and is contextualized, when selected conditions are met the user will be immediately notified of relevance. This second point is paramount because we cannot expect users to ask every question, rather, the data must find the data and the relevance must find the user.
Scenario: The military unit is moving from point A to point B. Along this vector a collection system has just recognized some significant changes (e.g., nine new trucks parked along side the road or the presence of RF [radio frequency] broadcasting that did not exist at any earlier time). Perpetual analytics can detect such conditions in real time and push such intelligence to the military unit – instantly.
Postings on this site don't necessarily represent IBM's positions, strategies or opinions. Jeff Jonas is the chief scientist of IBM Software Group's Threat and Fraud Intelligence unit and works on technologies designed to maximize enterprise awareness; Jeff also spends a large chunk of his time working on privacy and civil liberty protections. He will be writing a series of guest posts for Security Blog.
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