Core competencies are the basic cognitive building blocks that are required of a good intelligence analyst. Much has been written on the elements that make an individual an effective analyst and throughout this course you will have the opportunity to build these skills as well.
Table of Contents
Objectivity refers to the ability to be fair and impartial. It’s important for intelligence analysts to possess this core competency because if you approach analysis with a bias, you will produce slanted results.
One tactic to improve objectivity is war gaming, where you adopt one side of an analytic problem, while you face against another analyst who is adopting the other side. Ideally you will occasionally be “friendly” and occasionally “foe”, so that you can see how you reach different decisions than your colleagues and what biases may underpin those.
Independence of Political Consideration
This element is a special form of objectivity. Sometimes pressure from political figures can cause you to alter intelligence estimates for ones that are more favourable. This is not limited just to national security (as one might imagine a President or Secretary of State’s influence on a National Intelligence Estimate), but also occurs in Law Enforcement where the Police Chief may wish for intelligence projecting a decrease in organized crime even while data does not support this.
Timeliness should be obvious. Intelligence is useless if it is not produced or disseminated in time for those who rely on it to be able to take action based on it. Time management techniques or other workflow strategies (including automating elements of the task, where possible) may help.
Analytic standards are the elements of intelligence tradecraft that all analysts should be able to do. They are listed below.
Describe Quality and Reliability of Underlying Sources
The ability to assess the quality (usefulness of information) and reliability (likelihood of being correct) of underlying sources is an important element in producing good intelligence. The old adage “garbage in, garbage out”, applies here.
This element is particularly important in analytic techniques like Bayesian Analysis, where assessing reliability numerically is a requirement to using the formula.
Caveats and Expresses Uncertainties or Confidence in Analytic Judgement
Related to the previous point, if an analyst is uncertain (or very certain) about a specific piece of intelligence, they must be able to indicate that for the benefit of consumers. Given that an intelligence report may contain a variety of statements, with varying levels of confidence and classifications, clearly indicating the belief in or caveats about intelligence will help consumers act on this information with confidence.
The book An Introduction to Intelligence Research and Analysis (Clauser, 2008) provides a modified version of the Sherman-Kent table for expressing confidence in a judgement:
(For – Against)
|100 – 0||Certainty, No Estimate|
|99 – 1
85 – 15
|Almost Certain, Highly Likely|
|84 – 16
55 – 45
|Probable, Likely, Pobably, We Believe|
|51 – 49
50 – 50
49 – 51
|Chance Just Better Than Even, On Balance
Chances Are Even
Chances Just Less Than Even
|45 – 55
16 – 84
|It is Doubtful, We Doubt, Improbable, Unlikely, Probably Not|
|15 – 81
1 – 99
|Almost Certainly Not, Highly Unlikely, Chances are Slight|
|0 – 100||Impossibility, No Estimate|
Distinguishes Between Underlying Intelligence and Analysts’ Assumptions and Judgements
This element refers to the need to clearly explain where something is supported by evidence and interpretation of that evidence and where something is explained by assumptions and judgement.
This can seem like a tricky one, because we imagine that all intelligence involves some level of assumption or judgement when we perform any analysis. The difference is that we usually don’t have all the elements required to make an analysis and must declare what we are filling in.
As an example, if we make a statement in an an analytical product that the Soviet Union is going to collapse in 1989 (as it did), we may cite a variety of regulatory changes, statements from key leadership and other economic, social and political factors that we believe will contribute to the dissolution of the Soviet Union. These things may require an assumption – that the economic system in the Soviet Union is not capable of being replaced.
If we believe this, but fail to state it, it may colour our analysis and make it more difficult for consumers of our products to act on them. Additionally, it makes critical thinking exercises more difficult because we haven’t separated out where we can brainstorm.
Incorporates Alternative Analysis Where Appropriate
Speaking of brainstorming, this element involves the capability to provide alternative explanations in highly charged, uncertain or controversial areas.
This must be approached carefully, it does your consumer no good to provided with two analyses that are in direct contradiction to each other with no context (in that case you might as well be a weatherman saying there’s a 50% chance of rain and a 50% chance of no rain.)
Instead, providing an alternative analysis by highlighting how a key piece of intelligence that the analysis hinges on allows the consumer to consider the possibility.
As an example, if one part of your analysis reveals your belief that Afghanistan irregular forces have obtained access to chemical weapons, but this is based on having received funding from another terrorist group whose origin has not been traced yet, showing how the absence of this funding would change the estimate (either by altering the timeline for them to obtain chemical weapons or by removing the possibility altogether) will make a more comprehensive analytical product.
Demonstrates the Relevance of the Product to U.S. National Security
This point is relevant to the ODNI’s mission, which is to provide strategic intelligence in support of US National Security. An analytical product that does not clearly indicate its usefulness to the consumers that an ODNI analyst has access to is a waste of precious resources.
In a different context, this element could be renamed “Demonstrates the Relevance of the Product to Consumers.”
Uses Logical Argumentation
Avoiding the use of bias and arguing your points logically is helpful in ensuring that your arguments are sound. This can include being able to structure and recognize valid argument forms, recognize fallacies and work through arguments systematically.
Exhibits Consistency of Analysis Over Time
This element, which is titled in full “Exhibits Consistency of Analysis Over Time or Highlights Changes and Explains Rationale” focuses on the reliability of your estimates. This use of the word reliable is in the scientific sense, where given the same analytic information you return the same result repeatedly, suggesting a systematic, scientific approach.
If, given the same (or very similar) raw data on a situation but your estimate is radically different each time, that may suggest that you are not following a structured or reliable process. In the case that other factors have changed, indicating those helps the consumer follow your train of thought.
Makes Accurate Judgements and Assessments
Finally, making accurate judgements and assessments comes to the heart of intelligence analysis. If the forecasts you make and your judgements of the situation are consistently incorrect, your intelligence is useless.
This is not to say that being wrong or something else happening that you had expected means your product was flawed – you are attempting to measure people, rapidly changing in an environment that is unpredictable and fraught with deception and manipulation.
Rather, if your assessment is wrong, it is important to review deeply to find out why, and to integrate that knowledge into future assessments to improve your intelligence analysis skill.