The Learner's Guide to Geospatial Analysis

The Structured Analytic Techniques "Toolbox"


Structured analytic techniques are simply a "box of tools" to help the analyst mitigate the adverse impact on analysis of one's cognitive limitations and pitfalls. Taken alone, they do not constitute an analytic method for solving geospatial analytic problems. The most distinctive characteristic is that structured techniques help to decompose one's geospatial thinking in a manner that enables it to be reviewed, documented, and critiqued. "A Tradecraft Primer: Structured Analytic Techniques for Improving Intelligence Analysis" (CIA, 2009) highlights a few structured analytic techniques used in the private sector, academia, and the intelligence profession.

Structured thinking in general and structured geospatial thinking specifically is at variance with the way in which the human mind is in the habit of working. Most people solve geospatial problems intuitively by trial and error. Structured analysis is a relatively new approach to intelligence analysis with the driving forces behind the use of these techniques being:

  • an increased understanding of cognitive limitations and pitfalls that make intelligence analysis difficult;
  • prominent intelligence failures that have prompted reexamination of how intelligence analysis is generated;
  • DNI policy support and technical support for interagency collaboration; and
  • a desire by policy makers who receive analysis that it be more transparent as to how conclusions were reached.

In general, the Intelligence Community began focusing on structured techniques because analytic failures led to the recognition that it had to do a better job overcoming cognitive limitations, analytic pitfalls, and addressing the problems associated with mindsets. Structured analytic techniques help the mind think more rigorously about an analytic problem. In the geospatial realm, they ensure that our key geospatial assumptions, biases, and cognitive patterns are not just assumed correct but are well considered. The use of these techniques later helps to review the geospatial analysis and identify the cause of any error.

Moreover, structured techniques provide a variety of tools to help reach a conclusion. Even if both intuitive and scientific approaches provide the same degree of accuracy, structured techniques have value in that they can be easily used to balance the art and science of their analysis. It is clear is that structured methodologies are severely neglected by the geospatial community. Even in the rare cases where a specific technique is used, no one technique is appropriate to every step of the problem solving process.

There are two ways to view the nature of these techniques. Heuer categorized structured techniques by how they help analysts overcome human cognitive limitations or pitfalls to analysis. Heuer's grouping is as follows:

  • Decomposition and Visualization: The number of things most people can keep in working memory at one time is seven, plus or minus two. Complexity increases geometrically as the number of variables increases. In other words, it is very difficult to do error-free analysis only in our heads. The two basic tools for coping with complexity in the analysis are to: (1) break things down into their component parts, so that we can deal with each part separately, and (2) put all the parts down on paper or a computer screen in some organized manner such as a list, matrix, map, or tree so that we and others can see how they interrelate as we work with them. Many common techniques serve this purpose.
  • Indicators, Signposts, Scenarios: The human mind tends to see what it expects to see and to overlook the unexpected. Change often happens so gradually that we do not see it, or we rationalize it as not being of fundamental importance until it is too obvious to ignore. Identification of indicators, signposts, and scenarios create an awareness that prepares the mind to recognize change.
  • Challenging Mindsets: A simple definition of a mindset is “a set of expectations through which a human being sees the world.” Our mindset, or mental model of how things normally work in another country, enables us to make assumptions that fill in the gaps when needed evidence is missing or ambiguous. When this set of expectations turns out to be wrong, it often leads to intelligence failure. Techniques for challenging mindsets include reframing the question in a way that helps break mental blocks, structured confrontation such as devil’s advocacy or red teaming, and structured self-critique such as what we call a key assumption check. In one sense, all structured techniques that are implemented in a small team or group process also serve to question your mindset. Team discussions help us identify and evaluate new evidence or arguments and expose us to diverse perspectives on the existing evidence or arguments.
  • Hypothesis Generation and Testing: “Satisficing” is the tendency to accept the first answer that comes to mind that is “good enough.” This is commonly followed by confirmation bias, which refers to looking at the evidence only from the perspective of whether or not it supports a preconceived answer. These are among the most common causes of intelligence failure. Good analysis requires identifying, considering, and weighing the evidence both for and against all the reasonably possible hypotheses, explanations, or outcomes. Analysis of Competing Hypotheses is one technique for doing this.
  • Group Process Techniques: Just as analytic techniques provide structure to our individual thought processes, they also provide structure to the interaction of analysts within a team or group. Most structured techniques are best used as a collaborative group process, because a group is more effective than an individual in generating new ideas and at least as effective in synthesizing
    divergent ideas. The structured process helps identify differences in perspective between team or group members, and this is good. The more divergent views are available, the stronger the eventual synthesis of these views. The specific techniques listed under this category, such as brainstorming and Delphi, are designed as group processes and can only be implemented in a group.

Others have grouped techniques by their purpose:

  • Diagnostic techniques are primarily aimed at making analytic arguments, assumptions, or intelligence gaps more transparent;
  • Contrarian techniques explicitly challenge current thinking; and
  • Imaginative thinking techniques aim at developing new insights, different perspectives and/or develop alternative outcomes. In fact, many of the techniques will do some combination of these functions.

These different groupings of the techniques notwithstanding, the analysts should select the technique that best accomplishes the specific task they set out for themselves. The techniques are not a guarantee of analytic precision or accuracy of judgments; they do improve the usefulness, sophistication, and credibility of intelligence assessments.