All knowledge can be categorized in four boxes, since the time of the Greek philosophers, many argue. The same process is used by some analysts of risk and implicitly guides everyday business behavior.
Whether something is known or unknown makes a difference for risk mitigation or management, and also provides a key rationale for developing greater organizational agility. But that requires work and commitment, as a rational executive is going to focus most of his or her time on dealing with “known known” types of risk.
The cost-benefit of preparing for other types of risk is so low that most will spend relatively little time on them, with the possible exception of “known unknowns,” where research might plausibly convert an unknown to a known.
A known known can be statistically modeled and behavior based on statistical odds of occurrence.
Processes we understand we know can be statistically modeled. We can make assumptions about likelihood. We can set insurance rates, for example, or devise plans to take market share from a specific competitor.
When there are processes we know about, but cannot predict, we conduct research to try and eliminate the uncertainty.
“Unknown knowns” involve processes we know exist, but do not deem relevant to us. There is little or no perceived risk, so organizations do not plan for or worry about such matters, as the risk is deemed so rare.
“Unknown unknowns” are quite dangerous, as nobody recognizes there is any danger. When “one does not know what one does not know,” any rational search for answers will be thwarted.
A known unknown cannot be accurately modeled, so an organization has to aim for agility, the ability to shift and change if and when the magnitude of an event is large.
It is impossible to plan, in practical terms, for an unknown unknown. These are the sorts of catastrophic changes which can imperil an organization’s existence.
Unknown knowns pose risk because an organization might be aware of the risk, but deem it so unlikely that nothing is done to prepare for such events.
The point is that organizational agility is a major capability for dealing with three of the four categories of risks: known unknowns; unknown knowns or unknown unknowns.
“Known knowns” are things we know that we know and understand. Presumably, risk is low as we understand something.
“Known unknowns” are things we realize that we don’t know or understand. Or perhaps a better way of describing this category is that there are matters we know, but are unclear about potential risks.
In either case--known knowns or known unknowns--people have some semblance of certainty as there are boundaries around risk.
The other two categories involve higher levels of risk, as uncertainty is greater.
“Unknown unknowns” arguably pose the greatest risk, as the existence of the risk factors is not understood, not seen, not believed to be risk factors. Perhaps the Covid-19 pandemic is an example of that.
“Unknown unknowns” are future outcomes, events, circumstances, or consequences that we cannot predict. We also cannot plan for them. We don’t even know when and where to search for them.
“Unknown knowns” are things that exist, influence lives and our approach to reality, but are not perceived to do so. Or we do not see their significance or we refuse to acknowledge dangers.
The issue is where to categorize a black swan event. A black swan is an extremely rare event with severe consequences. It cannot be predicted beforehand. Some might say a black swan is an unknown known. We know they happen, but we cannot predict them.
That categorization is based on the assumption that we know black swans happen, so we understand that much. But we still cannot predict when one will happen, or where.
Alistair Croll and Benjamin Yoskovitz used the Knowns and Unknowns framework in their book Lean Analytics to describe different ways of looking at data:
Known Knowns (facts): you use analytics data to check those facts against them.
Known Unknowns (hypotheses): can be confirmed or rejected with measurements.
Unknown Knowns (our intuitions and prejudices): can be put aside if we trust the data instead.
Unknown Unknowns (it can be anything!): are often left behind, but can be the source of great insight. By exploring the data in an open-minded way, we can recognise patterns and hidden behaviour that might point to opportunities.
This framework also is used by the Johari Window.
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