Disinformation (Part 2)

December 14, 2012

Disinformation is something that looks like information but is not.  For CI purposes, disinformation is incomplete or inaccurate information designed to mislead others about your target’s intentions or abilities.

Disinformation is not the same as puffing, which is an advertising “overstatement” falling short of fraud. In business, disinformation is created intentionally, aiming at misleading competitors and others with erroneous or exaggerated information. It can also be generated simply by concealing relevant information[1]. In either case, the disinformation is aimed at establishing false value judgments, creating erroneous impressions, diverting attention from defects or problems, or hiding facts.

Trying to decide whether a competitor is using it is important:

  • If you don’t consider whether a key piece of data represents disinformation, and, in fact, it does, this failure can be destructive. Moreover, you may not recognize its destructive effect until it is too late to counteract it.
  • If you look for the disinformation, you may not spot it. In that case, your CI analysis could be affected in a direction and to a degree you cannot predict.
  • You may find what you think is disinformation, when it is not really there. That means you simply become more suspicious about the credibility you assign to what is really accurate data and more reluctant to rely on it without further confirmation. Not much of a cost.
  • You may be correct in spotting disinformation. In that case, handling it properly allows you to avoid its damaging effects on your CI analysis.

If you have identified data that appears to be disinformation, handle it as follows:

  1. Is the reason for your concern the source of the data or the nature of the data itself? If your concern is due to a questionable data source, you should look for other sources to verify the data. If your reason arises out of concern in the nature of the data itself, you should seek confirmation or contradiction from all sources, including the original source.
  2. If you are not sure whether the data is disinformation, try to estimate the likelihood of its accuracy and then explicitly assign a probability of accuracy to it. This may allow you to use the data, even while there is a question about its validity.
  3. Analyze why the potential disinformation was created, or allowed to continue. If you cannot see a reason why the source would have created it or permitted it to exist, it may not be disinformation. On the other hand, if you can determine why it may have been created or allowed to continue, you may not only have identified it as disinformation, but you may now understand what the source was trying to accomplish.
  4. If there remains any question about critical, non-confirmable data, treat it as disinformation.

However, don’t overreact. Be sensitive to the distinction between burnishing a corporate image and disinformation. Remember, the success of a disinformation initiative requires that you, the target, be willing to be deceived.

[1] It can happen by accident, but that is very rare.

One Comment on “Disinformation (Part 2)”

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