Uncertainty vectoring is a tool to systematically prioritize and visually organize environmental uncertainty. I designed this technique to address some of the shortcomings of scenario planning tools I’ve found over my years teaching and facilitating.
How does uncertainty vectoring work?
Uncertainty vectoring visually aligns high impact environmental uncertainties to allow decision makers to generate a range of “what if” scenarios. The technique helps clarify correlations between uncertainties and reveal the potential blind-spots of strategic actions.
Step 1: Define the destination.
We need to define our focus before we can do any useful work with uncertainties. The focus needs to include the area of interest and time horizon.
Step 2: Identify key uncertainties: The PEST+ brainstorming structure.
PEST analysis has been around for many years. The letters refer to key categories of external forces that organizations need to consider: political, economic, social, and technological. Its origins can be traced to the early work of Francis Aguilar in 1967.[i] Over the years, people have refined the categories in different ways and created numerous acronyms (STEPE, PESTLE, STEEP, STEEPER). These new formulations have added various categories such as legal, ecological, or environmental.
PEST is often referred to as PEST analysis, but this is a bit of a stretch for the word analysis. It is more of a memory device to help generate a diversity of forces and to ensure that you do not miss any key categories. PEST is often the first step in other forms of analysis or sense making, such as root cause analysis, scenario planning, or future state threat analysis.
I’ve used PEST-based techniques for many years and tried a number of different categories. I’ve settled on what I call the PEST+ framework. The four categories that have consistently generated the uncertainties with the highest impact are political/regulatory, economic/competitive, social/cultural, and technological/innovative. In addition, each specific application often has another category that is highly relevant to the situation. Examples could be environmental, legal, global, or even just “other.” In my facilitation experience, I have found including an “other” category often generates some helpful additions to the other four categories that were almost missed in the initial brainstorming.
Step 3: Identify the ten highest-impact uncertainties.
High-impact uncertainties are those that will have the greatest impact on the end point and the path to that end point within the time horizon defined in step 1. There will be disagreement about this. After all, these are uncertainties. It is not important to rank the exact order of impact. Sorting into high-, medium-, and low-impact categories will suffice.
Step 4: Identify the current state on the uncertainty continuum.
For each of the high-impact uncertainties, define the end points of the range and place an X on the point in that range that represents the current state.
Step 5: Create the uncertainty vector chart.
The uncertainty vector chart is the primary output of the uncertainty vectoring exercise. It creates a visualization of uncertainty relationships. The resulting chart gives a group a tool for organizing and discussing a complex mix of high-impact uncertainties. The generation of the chart creates a way to structure a conversation about the important but unknown elements of the industry.
We start with a blank white space. A flip chart will work, although I have found a whiteboard or two combined with flip charts often work better. In the center of the space place an X.
Pick one of the ten high-impact uncertainties in which the current state X, created in step 4, is close to the center of the uncertainty line. Set this line as your horizontal anchor and mark the end points to indicate directionality.
Measure the length of the line. It is important that you draw each subsequent uncertainty line to the same length as this first line.
Next, from the remaining nine uncertainties, choose a second high-impact uncertainty that is the most highly correlated with the first selected uncertainty. Add this uncertainty to your map. Notice the current state X created in step 4 is lined up with the X in the center of the uncertainty vector map. Pay attention to the directionality of the correlation with the first uncertainty and align your lines accordingly (make certain the +/- poles reflect the nature of the relationship with the other uncertainties).
Continue this process with the third uncertainty. Each time you add a new uncertainty to the map, discuss correlations with your group. It is in this process of deciding the order of adding the uncertainties and the strength and directionality of relationships among the uncertainties that your group begins to develop a deeper understanding of how these uncertainties interact with each other.
Continue this process until you have added all ten uncertainties to your map. It is likely that there will be some disagreement within your group about how uncertainties relate to each other. That is to be expected. After all, you are talking about uncertainties in future states; by definition, these positions are difficult to measure and predict. Work to find some compromise, but also make note of those uncertainties that are particularly difficult to place on the map. Your team may want to devote some additional time to think about these uncertainties or to discuss what additional data you could collect to generate a better understanding of them.
For more on Uncertainty Vectoring
This summary of uncertainty vectoring is adapted from Unquestioned Brilliance: Navigating a Fundamental Leadership Trap.
For more on the use of uncertainty vectoring and how it relates to scenario planning see Chapter 4 of Unquestioned Brilliance.
I enjoy facilitating this technique but it can be a complex technique to facilitate. It can trigger fascinating insights about industry shifts and strategic priorities. Don’t hesitate to reach out to me for facilitation assistance.
[i] Francis J. Aguilar, Scanning the Business Environment (New York: Macmillan, 1967).