Stephen Marrin Post-revision draft18 July 2011. Original draft submitted to Intelligence and National Security on 4 February 2011. Accepted for publication on 24 May 2011 pending minor revision.
Dr. Stephen Marrin is a Lecturer in the Centre for Intelligence and Security Studies at Brunel University in London. He previously served as an analyst with the Central Intelligence Agency and US Government Accountability Office. Dr. Marrin has written about many different aspects of intelligence analysis, including new analyst training at CIA‘s Sherman Kent School, the similarities and differences between intelligence analysis and medical diagnosis, and the professionalization of intelligence analysis. In 2004 the National Journal profiled him as one of the ten leading US experts on intelligence reform.
Abstract: Each of the criteria most frequently used to evaluate the quality of intelligence analysis has limitations and problems. When accuracy and surprise are employed as absolute standards, their use reflects unrealistic expectations of perfection and omniscience. Scholars have adjusted by exploring the use of a relative standard consisting of the ratio of success to failure, most frequently illustrated using the batting average analogy from baseball.Unfortunately even this relative standard is flawed in that there is no way to determine either what the batting average is or should be. Finally, a standard based on the decision makers’ perspective is sometimes used to evaluate the analytic product’s relevance and utility. But this metric, too, has significant limitations. In the end, there is no consensus as to which is the best criteria to use in evaluating analytic quality, reflecting the lack of consensus as to what the actual purpose of intelligence analysis is or should be.
Evaluating the quality of intelligence analysis is not a simple matter. Frequently quality is defined not by its presence but rather by its absence. When what are popularly known as intelligence failures occur, sometimes attention focuses on flaws in intelligence analysis as a contributing factor to that failure.