Topic 7.4: Qualitative risk analysis impact
Once again, one represents a very low impact on the project, and five indicates that it is very high or severe for our project.
An example of the language we might use is:
Insignificant – 1
Minor – 2
Moderate – 3
Major – 4
Massive – 5
You can choose any labels or scoring system you like here, as long as it corresponds to your probability scale; in other words, you cannot have probability scored out of five and impact out of 100 if you want your subsequent analysis to work.
Note that you should avoid terms such as ‘catastrophic’ that solely imply a negative impact – remember, we are analysing opportunities as well as hazards.
Unlike probability, impact is a multi-faceted concept.
The same risk might have no impact on the schedule, a minimal impact on budget, but a very high impact on health and safety.
For that reason, we consider (and therefore need to define) impact across a range of outcomes – examples follow…
These are just examples – you may need to modify or update the definitions to suit the context of organisational or project setting.
For example, you will need to better define positive impacts (opportunities).
You might also identify and define other impacts that are specific to the circumstances of your project and organisation.
Our project’s sponsor has suggested that if we use unskilled labour (the cause) we can speed up the construction of our stadium (the risk, or opportunity in this case) to save time and money (the effect).
To that end we have consulted with stakeholders and analysed the various impacts to arrive at the following risk impact scores:
Note that we have rated opportunities positively and hazards with a negative score.
As our final impact score needs to be on the same scale as our probability score (out of 5, in this case), we can’t just add the scores up to get a result.
Do we therefore take the mean, median, mode or make a three point estimate?
Actually, none of the above – when determining impact we take the largest score, or (if we are scoring hazards with a negative integer and opportunities with a positive) the score that is the furthest distance from zero.
Therefore in our example, when all impacts are considered, for the purpose of prioritisation (which is our next step) the impact score would be negative five (-5).
It is worth noting too that because we can often have multiple causes for a single risk, a similar shorthand can be applied to our rating of probability, as long as the impacts on the project are likely to be the same in case, regardless of cause.
For example, the risk of someone not showing up to work on the project may be caused by any number of things.
You can see with probability that all scores are expressed as positive numbers.
In this hypothetical example, the probability rating ultimately assigned to the risk of a project team member being absent from duty would be 4 out of 5.
In this example, the impact on the project will be the same no matter what the cause; that is, the absent team member will not be able to complete their work.
Note, however, that if different causes of a risk will result in different impacts, you should avoid this aggregation.