An article by Scott Swanson From the archives of Competitive Intelligence Magazine

Many intelligence practitioners embark upon collection efforts and comprehensive analysis without an accurate understanding of what they are chartered to investigate. This can yield the “failures” we hear of so often, which may have been a success in their own right, but just didn’t address the proper issue. The largely universal problem of defining intelligence requirements exist within intelligence departments in both the public and private sectors.

Vague requirements inevitably require constant revisions and expenditures of time and resources. Actionable requirement definitions create dynamics from which suitable expenditures can be made, personnel can be deployed, and procedures can be developed that will reduce or eliminate waste and even mitigate an intelligence failure. When a “felt” existence toward some semblance of direction is established, the requirement can become the target for creativity and tenacity in either collection or analysis.

Interesting Isn’t Intelligence

Determining a specific intelligence topic and the context by which decisions will be made, or how the inquiry was derived, helps direct the intelligence tasking process. Curiosities do not always warrant intelligence efforts if nothing is going to be done with the findings.

To avoid such wasteful situations ask customers how they are going to use the information. Is it actionable or just a curiosity? If this seems too intimidating or confrontational, at the very least use the Five W’s (who, what, when, where and why) as a guideline:

       • Who is this intelligence product for?

       • What is the focus?

       • When is it needed?

       • Where will the findings be presented?

       • Why is this requirement being requested?

Without direct answers to these questions, the effort could be a worthless chase or a disaster waiting to happen.

Research Methods

Before an intelligence project can get started, certain fundamental components must exist to create an understanding of the task at hand. Collection (and therefore analysis) is particularly challenging when the terms used to describe and explain a task are not clearly defined. At this point, the individual gathering against a specific requirement needs to be thinking fairly systematically. While non-linear thinking is principal in much of analysis, formal thinking and analysis.

       • Understanding the dynamics and problems and help in
         comparing strategies to deal with them

       • Critically evaluating studies and arguments

       • Designing research that attempts to answer the key questions

Terms may be used to enlighten, persuade or mislead. Take, for instance, this statement: “The U.S. economy was stronger during the 1990s than it was in previous years.” It does not define what stronger means. Therefore, anyone who agrees with the statement can look for whatever evidence they can to support it; those against the statement can find compelling evidence as well. If the obvious choice appears to be working the more quantifiable question, why then do corporate intelligence practitioners accept such tasks as:

       • I need you to find out what the market thinks of our customers
          product line.

       • What is our competitor’s hidden strategy?

       • What is the competitor’s overall budget?

In these examples, the intelligence practitioner needs to apply direction to the question. If possible, restate the question or tasking towards something tangible that can be directly observed and measured (e.g. housing, weapons, money, automobiles, manufacturing processes).

Developing the Questions

The worst tasking bases the research foundation on “normative statements.” Normative statements revolve largely around impressionable words like ought or should, bad or good:
 

       • Should we reduce our prices? (Normative)

       • Would it be wrong to reduce our prices? (Normative)

       • What impact would reductions in prices have on the budget?
         (Empirical)

The empirical question asks for measurable evidence that is relevant to the original value statement. A good empirical question asks for a description in the form of general patterns and tendencies, and also asks about the reasons or explanations for the patterns.

Once the question is structured properly, then the intelligence practitioner must ensure that everyone is working with the same definition and mental images. I find this especially true when
multi-national or multi-lingual resources are involved. A more formal definition of the question solidifies the meaning in relation to the collection and outcome. It also includes some criteria for measuring the existence of the findings and identifying variables.

Indicator Development

Let’s look now at how this refined requirements’ definition process can continue to evolve and improve the intelligence deliverable. The lists of defined indicators relate back to the assignment and are based on standard and non-standard potential events that may correlate to the intelligence topic. In compiling indicator lists, analysts draw on three major sources of knowledge:

       • Logic or longtime historical precedent

       • Specific knowledge of the subject matter concerned

       • Lessons learned from recent experience which correlates to the
         present situation.

Now the collection process has a gauge to measure the degree of relevancy with acquired information. The indicator list can also be linked to emerging trends. When trends (or anomalies) are spotted before an activity occurs, you have successfully provided some aspect of early warning.

In an ever-changing world, the point of providing intelligence is to illustrate possibilities in the future. Identifying the possibilities is valuable if they have been well though out, are based on fact, and are compelling enough for more thought or direct action. By starting off with better questions leading to better tasking, you create proper direction to spot indicators, with indicators contributing to elements found in threats.

[Editor’s note: this article was excerpted from the author’s original article in Competitive Intelligence Magazine, v8n6, November/December 2005.]

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