Intelligence_ From Secrets to Policy - Mark M. Lowenthal [108]
The underlying question is the expectations of either Congress or the DNI’s office about how these standards might affect future analysis. It is possible, for example, to perform highly in each of the standards and still find, after the fact, that the judgments and assessments proved to be inaccurate. Value is given to consistency, which can run counter to the desire for analytic insight and the avoidance of groupthink. If the highest standard for analysis is accuracy, then we face the problem that neither these standards nor any others will guarantee that outcome. Clearly, these standards are more likely to result in analytic products that are sound in terms of methodology, but this is not the same as accuracy. Also, these standards run the risk of creating a very mechanistic approach to what is, at its core, an intellectual process. For example, the truly gifted and occasionally insightful analyst could get poor grades in most of these criteria and still produce an accurate and useful analysis.
ANALYTIC TRANSFORMATION AND THE ANALYTIC WORKFORCE. The Deputy DNI for Analysis has embarked on a broad program called analytic transformation, which seeks “to change how we [intelligence analysts] approach analysis.” The initiatives fall into three broad areas: enhancing the quality of analysis; providing more effective community-level management; and offering more integrated analytic operations. The main drivers appear to be the sense that the community and the analysts’ data and products are not called on to the fullest extent.
Analytic transformation has several initiatives, including new approaches to training, new standards for producing analysis (such as product evaluation, source citation), and especially initiatives intended to get a better sense of community activity and to foster greater collaboration. Several of these latter initiatives have received a fair amount of attention in the media, including the Library of National Intelligence, which will be a central virtual repository of all disseminated intelligence. regardless of classification; A-Space, a common collaborative workspace for all analysts, similar in concept to shared networking Web sites available to the public: and Intellipedia, another collaborative Web space in which analysts can update and annotate other’s work at various levels of classification. Advocates see these as improving collaboration and also note that they will instantly be familiar to the young workforce (those with three years experience or less), which now represents about half of the analytic cadre. These various initiatives have also been controversial, with some veteran analysts asking how these various steps will actually improve the content of analysis and what the benchmarks will be.
The workforce demographics are driven largely by the contraction that the intelligence community endured during the 1990s, suffering deep budget cuts after the cold war. The so-called cold war peace dividend fell more heavily on intelligence than it did on defense. As DCI Tenet expressed it, the net result was the loss of 23,000 employees and positions across U.S. intelligence, meaning both people who left and—more significantly—people who were never hired. In the aftermath of the 2001 terrorist attacks, all agencies began major hiring efforts. The result of these efforts has been a workforce of decreasing experience over time as new hires outnumber veterans, who continue to retire.
These demographic trends have several important implications for analysis:• Experience: The most obvious issue is the relative inexperience of the workforce as analysts and subject matter experts. As discussed earlier, human intelligence (HUMINT) collectors need five to seven years to be considered seasoned. There is no agreed benchmark for analysts, but the five-year mark is probably a reliable