My colleague Jeff Jonas has written an important essay on the “Christmas Day Intelligence Failure”, the first of at least two parts: “The Christmas Day Intelligence Failure – Part I: Enterprise Amnesia vs. Enterprise Intelligence”. It’s an interesting essay, and well worth a read.

As readers of this blog likely know, there is similar interest in the elections community in data matching, in particular as regards state voter registration databases. Thad and I wrote a number of years ago about the potential value for election administration of data exchange standards (“The Next Big Election Challenge: Developing Electronic Data Transaction Standards for Election Administration”). More recently, I was part of a team (including Jonas) that produced a paper on an intrastate voter registration data matching project, in collaboration with election officials from Oregon and Washington: “Interstate Voter Registration Database Matching: The Oregon-Washington 2008 Pilot Project.” Also, Jonas and I were part of the National Academy of Sciences committee that examined state voter registration databases in detail, including a lot of study of interoperability and intrastate matching.

In his Part 1 essay on the “Christmas Day Intelligence Failure” Jeff talks about “enterprise intelligence”, and he describes how an intelligent system would have identified the suspect:

What would analysts and policy makers expect from an “intelligent” system? Abdulmutallab applies for a multi-entry visa. The terrorist database (TIDE) is checked and found to contain no such record. The State Department issues a visa. Later, a TIDE record for Abdulmutallab is added to TIDE. The split-second this record is added to TIDE, the State Department is notified the visa may need reconsidered. (Was there enough evidence for revocation?) I believe when the dust settles and the forensics analysis is completed, whether it is open source or other intelligence collection, it will be clear Abdulmutallab would not have made it onto that plane, so long as this additional fodder was made discoverable.

While I think there is still a lot that we need to learn about the databases the intelligence community uses, and in particular what sorts of matching algorithms they employ, the important lesson for election administrators and scholars at this moment in time is that as we begin to design systems for database matching using state voter registration data, we ought to make sure that they are being designed to have the very intelligent properties that Jeff discusses in his essay.