When we look at risks – and opportunities – across Africa, there are things that fit into each of those categories. There are also unknown knowns (things we do know about but choose to ignore). Risk Classification: Known-Knowns, Known-Unknowns.Donald Rumsfeld famously talked about known knowns (things we know we know), known unknowns (things we know we don’t know) and unknown unknowns (things we don’t know we don’t know).PMP Protein: Understanding Brainstorming.This four-quadrant classification is shown in the below figure. In this case knowledge does not exist within the community or the sphere of influence of the risk manager. Unknown-unknowns: These are “unknown risks” - the unknown risks with unknown or unforeseen work.These are also called as “known risks” - known risks but with an unknown amount of rework. Known-unknowns: These are classic risks or risks what you as a project manager or risk manager most likely see.These align more closely with the ambiguity risks. These are also known as “untapped knowledge”. But someone else within the community may know about it and also know the amount of work needed.

Because they are not known (unknown), hence they are hidden from us.

Unknown-knowns: These are hidden facts.

These are also known as “tapped knowledge”. Hence, they are addressed as part of the project scope. These are known facts and/or requirements with known amount of work.

Known-knowns: These are not risks! But these facts and requirements.

Now, let’s see the four-quadrant classification of risks. Hence, you are not clear how this new regulatory development is going to impact your project. But you don’t have perfect knowledge on it. Taking an example: let’s say there is a new regulatory framework expected. Ambiguity, as the name itself informs, is about the lack of understanding or uncertainty in meaning. Hence, we have uncertainty about results in this case, which is variability.Īmbiguity, on the other hand, is because of lack knowledge or imperfect knowledge. But we did not know until we did the testing. The variability here is this: the number bugs can be low or high. You are conducting testing for a phase of your project and a large number of bugs are found during testing, which was much higher than expected. Let’s take an example to understand variability first. Variability is primarily about the uncertainty of results, whereas ambiguity is about the uncertainty of meaning. Variability and ambiguity are two aspects of uncertainty.

The first point above, i.e., available information about the risks is quickly understood.

Known unknowns