The U.S. Congress has refused to give the FBI $11 million to expand the use of data mining in counter-terrorism efforts. American politicians are generally hostile to government use of data mining, a technique widely used, for decades, in business (marketing), law enforcement (catching criminals) and the military (finding the enemy). This last use has become much more sophisticated since the U.S. Department of Defense began pouring billions of dollars a year into finding ways to defeat IEDs (improvised explosive devices, usually roadside bombs). The effort to lower IED casualties has opened up all sorts of opportunities for technological innovation. No one harasses researchers for using data mining, or makes fun of building supercomputers with graphics processors (often the same ones found in video game consoles, making super-fast computers cheap enough to be used in a combat zone to make life saving predictions), when it saves troops from getting killed.

The data mining was initially used to figure out who the bomb making crews were, and where they operated from. Then, using math techniques first developed during World War II, the intel geeks began creating predictions about where IEDs were most likely to show up next. These predictive models get better as the quality of the information going into them improves. As more terrorists are captured and interrogated, and their computers and data is translated, the predictions become more accurate.

Using more primitive computers, Germany employed data mining successfully in the 1970s, to find leftist, middle class terrorists who were operating with assistance from the East German secret police. The terrorists thought they were well concealed, but data mining can do wonders with the slightest pieces of information.

The FBI has been unable to make this point to Congress, mainly because some key legislators are ideologically opposed to data mining, and refuse to acknowledge the widespread success of the technique in civilians and military sectors.