Do we know what we do not know?

The fast pace of change creates unpredictability and makes it harder to accomplish the mission of satisfying stakeholders. And indeed, organizations must deal with established and new competitors, shortening cycle times, the furious pace of innovation, and socio-economical and geo-political profound transformations. In such a deeply changing environment, it’s getting harder and harder to predict the future. Hence, it’s always a good start to clearly separate what we know from what we do not know. Then, we want to build our learning path to move the unkonwns into the knowns territory, and adapt our decision/making process depending on what is known, and what is unknown.

Rumsfeld matrix
Fig. 1 Known/Unknown Matrix

I am using here a mental model built with the known/unknown matrix (Fig. 1) as largely inspired by an original speech of Donald Rumsfeld and the many complex systems scholars and analysts:

  1. Top left we have Known Knowns. That’s the mission, the why, and what we are supposed to be doing. That’s the future state we want to get to. Here we have empirical data, targets, requirements, and facts to deal with, as well as skills, methodologies, and techniques.
  2. Top right we have known unknowns: the known complications, deviations, and problems that we still need to solve. We are well-equipped to deal with problems and criticalities in this quadrant. We use a set of well-known and consolidated tools to solve, limit or eliminate them.
  3. Then, on the bottom left quadrant, Unknown Knowns. That’s existing knowledge and resources we need or will need in the future but we are not aware of them. This knowledge can be found or bought within the organization or the whole value chain. There is no need to reinvent the wheel. Once we got this new knowledge, we move to the known unknown quadrant, and we use it to solve problems (known/unknown quadrant) or redefine our way of working (known/unknown).
  4. Finally, the bottom right quadrant includes the unknown unknowns. These are things we do not see coming, we cannot predict them, and often we don’t even know they exist until they show up. They can be black swans, highly improbable and unpredictable events, think for instance COVID-19, or a new discovery available. Once they appear, they could give feedback on new facts, problems, and deviations (quadrant top left known knowns) and cause new knowledge gaps to emerge (bottom left, unknown knowns)

So what?

It’s the nature of complex work to deal with unknowns. But lean-agile organizations leverage hypothesis testing, fast feedback, problem-solving, empiricism, transparency, and continuous learning to increase their resilience, and reduce complexity. They use different decision-making approaches, depending on the context and typology of unknowns they deal with, thus being able to thrive in complex ecosystems. When there are great unknown areas agile fits better, while more deterministic approaches are appropriated when variables are defined, and we know from the start what problems we face, and how to solve them