Data quality, sometimes referred to as precision, defines the extent of one’s current knowledge and understanding of a risk. More specifically, it defines the level of confidence placed in the estimates of impact and likelihood.

While data quality does not tell us anything in itself about the severity of the risk, it does tell us how much we can trust our assessment of that severity. For example, consider the risk of an archeological discovery while excavating a construction site. If this were to happen to a company working on a site in a major North American city, you know that this would have a very high impact, since there would be a significant delay while the discovery was examined. It is possible to estimate the probability of this occurrence by examining the frequency of similar events in the area. Intervention difficulty can be accurately assessed as very high, since there is little that the construction company can do to speed matters. We can therefore characterize our assessment of this risk as being of high precision.

Now consider the same company working on a site in a less-developed part of the world with which the company is not familiar. In the event of an archeological discovery while excavating, do you know what the reaction of the responsible authorities would be? Who are the responsible authorities in such a case anyway? Has the site you are excavating been used for human habitation for the last 5,000 years, or has it only been in use for the last 50 years? In this case, your estimates of impact and likelihood, and hence of risk severity, will be much less precise.

A typical scale for characterizing the quality of risk assessment data is shown below.

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