Alternate Terms (Primary Users):

Sensor Fusion (Dept of Defense).
Situational Awareness (Dept of Defense).
Remote Sensing (Dept of Defense).
Filter Screener (Financial Analysis).

Misnomers:

Selective Director does Feature Selection, Dimensionality Reduction, and Computer Data Mining, but the reverse is not true.

Example Concept:

Contrary to popular belief, there is no shortage of available sensors, data, or facts.  Sensor fusion is not simply cluttering the view with layer after layer of corroborating data images.  The shortage instead is with understanding and using the sensors, data, and facts.  

The key to understanding the data is in asking the right question. Only then can the analyst decide what sensor, what data, and which facts to direct and where in support of a specific goal.  In organizational terms, this means even the Private in charge of the sensor needs to have a basic understanding of what the General needs to know.

Neurological Evidence:

Did you know that for every single inbound (afferent) sensory neuron, there are 10 more outbound (efferent) director neurons? Quantitatively, this means less than 10% of the neurons' job is about collecting and mining random sensory facts and over 90% about modulating and directing towards what the sensors must be tuned.

Typical satellite (remote) sensors download massive swathes of data.  Elaborate computer programs register and stitch (but not fuse) the data images together.

But the images are meaningless until we know our primary goal.  Is our job to prepare against storm surges?  Or prevent traffic jams?  Or mitigate epidemics?  Only then can we direct and fuse our sensor selection into the big picture.