In my role of business consultant I often find myself preaching to clients to remain rational and not lose their heads. It is only natural that when we feel such heightened uncertainty, our decision-making processes can break down. We may become paralysed and afraid to act, or we may act on the basis of bias, emotion and intuition…… instead of logic and facts.

Being aware of our uncertainty is a necessary precursor to managing it. Effective awareness means pausing, taking a strategic stop and assessing the situation and the unknowns. We’re now being confronted with data that looks actionable — even though logically, we know it’s incomplete and volatile. But even when knowledge is limited, we have tools to help us make decisions systematically and analytically. Whether we’re assessing the meaning of the latest unemployment numbers or latest drop in air travel statistics, we can use a simple four-step process to work with and through ambiguity to make careful, reasoned decisions.

1. Identify the category of historical data you are working with.
   There are three main kinds of data we often confront and feel compelled to act on: (i) salient data – which captures our attention because it is noteworthy or surprising (ii) contextual data, which has a frame that may impact how we interpret it (iii) patterned data, which appears to have a regular, intelligible and meaningful form.
2. Recognise which mental biases are triggered by each data category
   Different kinds of data trigger different biases, so identifying the data type and its related bias makes it easier to escape mental mistakes.

• Salient data can activate salience bias, in which we overweight new or noteworthy information, resulting in suboptimal decision-making, planning errors and more confusion. For example, all the data we are getting on the drop in air travel, because of Covid-19-related travel restrictions, might make us think that travel as we have come to know it is finished — but in reality, this one salient piece of data tells us almost nothing about future travel.
• Contextual data can constrict our thinking and lead to a framing bias: The context in which we receive the data impacts how we think about it. For example, “80% lean ground beef” sounds more healthful than “beef with 20% fat.” But it’s the same beef, framed differently.
• Patterned data often prompts the clustering illusion, whereby we assume that random events are information that will help us predict a future event. The human brain is wired to look for patterns, sometimes when they don’t exist. Equally important, when patterns do exist, they often don’t have predictive value. A die that turns up a two several times in a row has established a pattern, but that says nothing about what the next roll will be.
  

Recognising how each of these categories triggers our biases can prevent us from falling prey to those biases, but how do we move forward once we’ve accepted that we need additional information or insight to confidently make decisions about the future?

3. Invert the problem to identify what you really need to know.
   The third step in the process is to realise that you don’t need to know everything, but you do need to identify what matters most to your decision-making. To do that, invert your problem solving. Begin at the end, asking: So what? What do I really need to know to understand the situation? What difference would this information make? And how do I expect to use it? The universe of “known unknowns” — those pieces of data that exist but are not in your possession — is endless. But you don’t need to explore them all; inversion can help you home in on those you deem to be critical to solving your specific problem with confidence. For example, the salient data about diminishing airline demand triggers a visceral response, which can make it easier to conclude that the industry is permanently in dire straits. However, if you step back, ans think things through, you can surely conclude that there will continue to be an airline industry — that in the long term, people will want mobility, and the world’s economy will require it. This is a “known known.” There is so much we know to be unknown. But there’s good news: To solve a specific problem, you don’t need to probe all the unknowns. To stay with the mentioned air travel example, this is true whether you are deciding whether to get on an airplane or to invest in an airline. A traveler’s concerns would be whether and when there is a flight to the desired destination and whether it feels safe to take it, whereas an investor might focus on which airline is best positioned to survive the downturn. Either way, by inverting your problem you can focus on the known unknowns that matter to you.

4. Formulate the right questions to get the answers you need.
   Many of us have trouble carving out the questions that could help us make a decision. One useful and practical way to move forward is to organize your questions into four main categories: behaviour, opinion, feeling and knowledge. This ensures that you’ll bring both distance and a variety of perspectives to the way you probe your data, which will help you debunk preconceived assumptions and judgments. It will also give you a better context for interpreting the answers, because you’ll know the lens through which they are being filtered.

• Behavior questions address what someone does or has done and will yield descriptions of actual experiences, activities and actions. If you’re assessing the state of the airline industry, you might ask: Who is still traveling? How does that extrapolate to a larger customer base?
• Opinion questions tackle what someone thinks about a topic, action or event. They can get at people’s goals, intentions, desires and values. In the airline example, you might ask: Is it currently safe to travel? Are the airlines taking proper precautions?
• Feeling questions ask how someone responds emotionally to a topic. They can help you get beyond factual information to learn what people may be inclined to do regardless of the data. Here, you might ask: How safe do travelers feel? How safe do airline employees feel?
• Knowledge questions explore what factual information is available. While some may argue that all knowledge is a set of beliefs, knowledge questions assess what one may consider to be factual. You might ask: What routes have been paused or cut? How many more will be cut? Have there been Covid-19 transmission cases linked to flying?
  

The four-step process helps you better address your emotional responses, name and confront them and move forward with a RATIONAL DECISION. It will help you obtain have a more complete picture, reducing the likelihood that you rely upon cognitive biases. We’ll never know the future, but by examining our data and our thinking we can develop and ask great questions that will allow us to more confidently make decisions amid uncertainty.